Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
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Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"4651",leadTitle:null,fullTitle:"A Fresh Look at Anxiety Disorders",title:"A Fresh Look at Anxiety Disorders",subtitle:null,reviewType:"peer-reviewed",abstract:"This book, the ideal following of the previous New Insights into Anxiety Disorders, collects papers of a number of clinical psychiatrists all over the world, giving their contribution to the comprehension and clinical management of anxiety disorders. Following the previously edited book on anxiety, this new one will focus on some specific clinical issues such as PTSD, psychosomatics, and complementary approaches to anxiety management themes which were not discussed in the previous book.",isbn:null,printIsbn:"978-953-51-2149-7",pdfIsbn:"978-953-51-7254-3",doi:"10.5772/59525",price:139,priceEur:155,priceUsd:179,slug:"a-fresh-look-at-anxiety-disorders",numberOfPages:358,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"8f18ff3698fbd7584a3da8a3e4916fba",bookSignature:"Federico Durbano",publishedDate:"September 9th 2015",coverURL:"https://cdn.intechopen.com/books/images_new/4651.jpg",numberOfDownloads:42134,numberOfWosCitations:50,numberOfCrossrefCitations:57,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:97,numberOfDimensionsCitationsByBook:3,hasAltmetrics:1,numberOfTotalCitations:204,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 22nd 2014",dateEndSecondStepPublish:"November 12th 2014",dateEndThirdStepPublish:"February 16th 2015",dateEndFourthStepPublish:"May 17th 2015",dateEndFifthStepPublish:"June 16th 2015",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"157077",title:"Dr.",name:"Federico",middleName:null,surname:"Durbano",slug:"federico-durbano",fullName:"Federico Durbano",profilePictureURL:"https://mts.intechopen.com/storage/users/157077/images/system/157077.jpeg",biography:"Dr. Federico Durbano received a degree in Medicine with a specialization in Psychiatry. He has worked at various hospitals, including Milan “Ospedale Maggiore Policlinico,” Treviglio, Melegnano, and Fatebenefratelli, where he achieved significant career milestones. He is currently the director of the Mental Health and Substance Abuse Department at ASST Melegnano e della Martesana. Dr. Durbano has had teaching assignments at the University of Milan (Nursing School) and the University of Castellanza (Master in Criminology). He has attended more than seventy local and national congresses and courses as an invited speaker and has published more than 180 papers. He is also a technical advisor to the court in the field of forensic psychiatry.",institutionString:"Mental Health and Substance Abuse Department in ASST Melegnano and Martesana",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"5",institution:{name:"ASST Melegnano e della Martesana",institutionURL:null,country:{name:"Italy"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1061",title:"Psychiatry",slug:"mental-and-behavioural-disorders-and-diseases-of-the-nervous-system-psychiatry"}],chapters:[{id:"48919",title:"Risk Factors of Anxiety Disorders in Children",doi:"10.5772/61169",slug:"risk-factors-of-anxiety-disorders-in-children",totalDownloads:3107,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Anxiety disorders are common; lifetime prevalence for the group of disorders is estimated to be as high as 25%. The main question is What is the relative contribution of genetics and environment to etiology of anxiety disorders? The anxiety disorders are not, from a genetic perspective, etiologically homogeneous. Structural equation modeling provides estimates of variance in liability to a disorder that is attributable to additive genetic, common familial environmental, and individual-specific environmental factors. Familial aggregation that largely results from genetic risk factors has been documented for all of the major anxiety disorders. Genes predispose to two broad groups of disorders dichotomized as panic-generalized-agoraphobic anxiety versus specific phobias. The candidate genes are the ones encoding the central and peripheral nervous system receptors and transporters. Trauma in childhood disposes to further anxiety disorders through the hyperactivity of the HPA axis and the hypersecretion of CRF. Traumatic experience in developmental age leads to neurobiochemical changes in brain, typical for panic disorder or PTSD. Behavioral inhibition in early childhood is a predictor of further anxiety disorders. Some types of parental behaviors and family environment can lead to them, as well as improper interactions between parents and child.",signatures:"Malgorzata Dabkowska and Agnieszka Dabkowska-Mika",downloadPdfUrl:"/chapter/pdf-download/48919",previewPdfUrl:"/chapter/pdf-preview/48919",authors:[{id:"48667",title:"Dr.",name:"Malgorzata",surname:"Dabkowska",slug:"malgorzata-dabkowska",fullName:"Malgorzata Dabkowska"},{id:"58055",title:"Dr.",name:"Agnieszka",surname:"Dabkowska-Mika",slug:"agnieszka-dabkowska-mika",fullName:"Agnieszka Dabkowska-Mika"}],corrections:null},{id:"48416",title:"Examining Sex and Gender Differences in Anxiety Disorders",doi:"10.5772/60662",slug:"examining-sex-and-gender-differences-in-anxiety-disorders",totalDownloads:3743,totalCrossrefCites:18,totalDimensionsCites:29,hasAltmetrics:1,abstract:"Several studies have examined sex differences in different anxiety disorders. Females are repeatedly found to be more likely than males to suffer from anxiety in general and to be diagnosed with most anxiety disorders, including agoraphobia (AG), panic disorder (PD), separation anxiety (SA), specific phobia (SP), social anxiety disorder (SAD), generalised anxiety disorder (GAD), obsessive-compulsive disorder (OCD), and acute and posttraumatic stress disorder (ASD and PTSD), although the latter three are technically no longer categorised as anxiety disorders according to DSM-5. This chapter provides an overview of research on sex and gender differences in anxiety disorders ranging from the well-established female preponderance in prevalence and severity to possible sex differences in the risk and protective factors associated with anxiety, sex differences in the clinical presentation of anxiety disorders, and potential sex differences in the effectiveness of different treatments. The chapter contains suggestions for future research, including important questions that remain to be answered.",signatures:"Dorte M. Christiansen",downloadPdfUrl:"/chapter/pdf-download/48416",previewPdfUrl:"/chapter/pdf-preview/48416",authors:[{id:"113525",title:"MSc.",name:"Dorte",surname:"M. Christiansen",slug:"dorte-m.-christiansen",fullName:"Dorte M. Christiansen"}],corrections:null},{id:"48428",title:"Psychobiological Aspects of Panic Disorder",doi:"10.5772/60663",slug:"psychobiological-aspects-of-panic-disorder",totalDownloads:2619,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Anxiety is a useful warning sign that helps an individual face potential or real danger. At appropriate levels, it serves as a warning for the presence of internal or external threats, causing a person to be alert and prepare to deal appropriately with such situations. Moreover, moderate levels of anxiety can lead to improved performance in several activities. However, anxiety becomes pathological when its duration is excessively long or its intensity is extremely high and leads to significant suffering and distress. In such cases, anxiety is appropriately described as part of a pathological response, characterizing an anxiety disorder. The historical concept of a unitary anxiety disorder has been replaced by a heterogeneous group of psychopathologies with different etiologies. Panic disorder is a complex anxiety disorder that involves both recurrent, unexpected panic attacks, and persistent concern about having additional attacks. The present chapter reviews current psychobiological perspectives in the etiology and treatment of panic disorder. The first section describes the current classification of this anxiety disorder. We then explore possible neural circuitry associated with panic disorder. Finally, the chapter addresses current treatment approaches, considering the efficacy of different forms of psychotherapy and pharmacological treatments.",signatures:"Daniel C. Mograbi, Vitor Castro-Gomes, Elie Cheniaux and J.\nLandeira-Fernandez",downloadPdfUrl:"/chapter/pdf-download/48428",previewPdfUrl:"/chapter/pdf-preview/48428",authors:[{id:"52728",title:"Dr.",name:"J.",surname:"Landeira-Fernandez",slug:"j.-landeira-fernandez",fullName:"J. Landeira-Fernandez"},{id:"52733",title:"Dr.",name:"Vitor",surname:"Castro-Gomes",slug:"vitor-castro-gomes",fullName:"Vitor Castro-Gomes"},{id:"174509",title:"Dr.",name:"Daniel",surname:"Mograbi",slug:"daniel-mograbi",fullName:"Daniel Mograbi"},{id:"175657",title:"Dr.",name:"Elie",surname:"Cheniaux",slug:"elie-cheniaux",fullName:"Elie Cheniaux"}],corrections:null},{id:"48437",title:"Genetics of Posttraumatic Stress Disorder — Candidate Genes and Their Implication in the Disease-Associated Molecular Pathomechanisms",doi:"10.5772/60443",slug:"genetics-of-posttraumatic-stress-disorder-candidate-genes-and-their-implication-in-the-disease-assoc",totalDownloads:1622,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:1,abstract:"Posttraumatic stress disorder (PTSD) is a complex psychiatric disorder (DSM-V code: 309.81; ICD-10 codes: F43.1). PTSD is an anxiety disorder developed in a person experiencing, witnessing, or learning about an extreme physically or/and psychologically distressing event. Its incidence and the number of this disease-affected people are threateningly increasing in contemporary society. Therefore, the development of prognostic strategies and novel efficient methods on early diagnostics and treatment of PTSD is currently considered as one of the most important healthcare problems worldwide.",signatures:"Boyajyan Anna, Avetyan Diana, Hovhannisyan Lilit and Mkrtchyan\nGohar",downloadPdfUrl:"/chapter/pdf-download/48437",previewPdfUrl:"/chapter/pdf-preview/48437",authors:[{id:"157186",title:"Prof.",name:"Anna",surname:"Boyajyan",slug:"anna-boyajyan",fullName:"Anna Boyajyan"},{id:"157188",title:"Dr.",name:"Gohar",surname:"Mkrtchyan",slug:"gohar-mkrtchyan",fullName:"Gohar Mkrtchyan"},{id:"174627",title:"M.Sc.",name:"Diana",surname:"Avetyan",slug:"diana-avetyan",fullName:"Diana Avetyan"},{id:"174628",title:"Dr.",name:"Lilit",surname:"Hovhannisyan",slug:"lilit-hovhannisyan",fullName:"Lilit Hovhannisyan"}],corrections:null},{id:"48867",title:"Posttraumatic Stress Disorder Biomarker — p11",doi:"10.5772/61073",slug:"posttraumatic-stress-disorder-biomarker-p11",totalDownloads:1535,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Post-traumatic stress disorder (PTSD) is a chronic and disabling anxiety disorder associated with a traumatic event [1]. It is linked to increased risk of suicide and deficits in social functioning [2, 3]. Despite extensive study in psychiatry, the underlying mechanisms of PTSD are still poorly understood [4, 5]. Currently, the diagnosis for PTSD is based on clinical observation and symptom checklist [4, 6-8] and no laboratory blood-based tests. Although biomarker discovery for PTSD is not easy [8], a reliable biomarker would significantly impact the diagnosis and therapeutic monitoring of PTSD. Developing interventions to identify and treat PTSD requires objective approaches to determining the presence of PTSD [8]. Substantial data indicate several potential biomarkers for PTSD. Of these candidate markers, p11 (S100A10) has been studied in PTSD animal models [7] and in human subjects with PTSD [6]. We found that p11 is over-expressed in both animal models and post-mortem brains of subjects with PTSD [7]. Incorporating testing of p11, a novel biomarker for PTSD, into clinical practice, along with more subjective measures, such as participants’ medical history, mental status, duration of symptoms, and symptom checklist or self-report, would provide additional power to predict impending PTSD. In this chapter, we discuss the biomarker concept and the potential clinical utility of PTSD biomarkers. We further discuss the potential of p11 as a PTSD biomarker and as a tool that may enhance PTSD diagnosis and intervention in health care practice.",signatures:"Lei Zhang, Xian-Zhang Hu, He Li, Xiaoxia Li, Stanley Smerin, Dale W.\nRussell, Angela Boutte, Berwin Yuan, Nora Wang, Ze Chen and\nRobert J. Ursano",downloadPdfUrl:"/chapter/pdf-download/48867",previewPdfUrl:"/chapter/pdf-preview/48867",authors:[{id:"30247",title:"Dr.",name:"Lei",surname:"Zhang",slug:"lei-zhang",fullName:"Lei Zhang"}],corrections:null},{id:"48385",title:"Anxiety Disorders and Suicide: Psychiatric Interventions",doi:"10.5772/60594",slug:"anxiety-disorders-and-suicide-psychiatric-interventions",totalDownloads:1872,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"A universal phenomenon equally ancient as the history of mankind, suicide is defined as the willful and intentional ending of one’s own life. Risk factors for suicidal behavior are traumatic childhood and adulthood experiences, negative interfamily interactions, social isolation, decreased social solidarity, financial troubles, losses, despair, impulsivity, and migration. Recognized as a critical public health problem, preliminary causes of suicide are financial, religious, political, social, cultural, and medical in addition to mental disorders like depression and alcohol addiction. It has been proven in a number of researches till today that there is a correlation between major depression, bipolar disorder, schizophrenia, borderline personality disorder, alcohol-drug use, and suicidal behavior. Nonetheless, the relation between anxiety disorders and suicidal behavior has not been clearly defined to date. The evidences gathered so far reveal that panic disorder is only an independent risk factor for suicide attempt. The limited number of studies on this domain provided nonhomogenous results. It is however a point to keep in mind that if anxiety disorders are codiagnosed with mental disorders, they pose risk for suicidal behavior. In different studies with a wider sampling in this domain, analyzing the effect of specific anxiety disorders on suicidal behavior might be useful for suicide prevention programs.",signatures:"Cicek Hocaoglu",downloadPdfUrl:"/chapter/pdf-download/48385",previewPdfUrl:"/chapter/pdf-preview/48385",authors:[{id:"28322",title:"Prof.",name:"Cicek",surname:"Hocaoglu",slug:"cicek-hocaoglu",fullName:"Cicek Hocaoglu"}],corrections:null},{id:"48400",title:"Comorbid Anxiety in Schizophrenia and Schizoaffective Disorder",doi:"10.5772/60643",slug:"comorbid-anxiety-in-schizophrenia-and-schizoaffective-disorder",totalDownloads:1531,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"There is some evidence that antipsychotic medication (Quetiapine) is somewhat efficient in reducing anxiety in schizophrenic patients.",signatures:"Delia M. Podea, Aurora I. Sabau and Karol J. Wild",downloadPdfUrl:"/chapter/pdf-download/48400",previewPdfUrl:"/chapter/pdf-preview/48400",authors:[{id:"30327",title:"Prof.",name:"Delia",surname:"Podea",slug:"delia-podea",fullName:"Delia Podea"},{id:"174601",title:"Dr.",name:"Aurora",surname:"Sabau",slug:"aurora-sabau",fullName:"Aurora Sabau"},{id:"174602",title:"Ms.",name:"Karol Julien",surname:"Wild",slug:"karol-julien-wild",fullName:"Karol Julien Wild"}],corrections:null},{id:"48398",title:"Joint Hypermobility, Anxiety, and Psychosomatics — The New Neuroconnective Phenotype",doi:"10.5772/60607",slug:"joint-hypermobility-anxiety-and-psychosomatics-the-new-neuroconnective-phenotype",totalDownloads:9353,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"In this chapter, after summarizing the concept and diagnosis of the Joint Hypermobility (Hyperlaxity), we review case control studies in two directions: Anxiety in Joint Hypermobility and Joint Hypermobility in Anxiety disorders, studies in nonclinical samples, review papers, and one incidence study. Collected evidence tends to confirm the strength of the association described two and a half decades ago. Common mechanisms involved include genetics, autonomic nervous system dysfunctions, and interoceptive and exteroceptive processes. Considering clinical and nonclinical data, pathophysiological mechanisms, and present nosological status, we suggest a new Neuroconnective phenotype in which together around a common core Anxiety-Collagen hyperlaxity, it includes five dimensions: behavioral, psychopathology, somatic symptoms, somatosensory symptoms, and somatic illnesses. Somatic illnesses include irritable bowel, dysfunctional esophagus, multiple chemical sensitivity, dizziness or unsteadiness (central vestibular pattern), chronic fatigue, fibromyalgia, glossodynia, vulvodynia, hypothyroidism, asthma, migraine, temporomandibular dysfunction, and intolerances or food and drug hypersensitivity. It is envisaged that new descriptions of anxiety disorders and also of some psychosomatic conditions will emerge and different nosological approaches will be required.",signatures:"Guillem Pailhez, Juan Castaño, Silvia Rosado, Maria Del Mar\nBallester, Cristina Vendrell, Núria Mallorquí-Bagué, Carolina Baeza-\nVelasco and Antonio Bulbena",downloadPdfUrl:"/chapter/pdf-download/48398",previewPdfUrl:"/chapter/pdf-preview/48398",authors:[{id:"53824",title:"Prof.",name:"Guillem",surname:"Pailhez",slug:"guillem-pailhez",fullName:"Guillem Pailhez"},{id:"174181",title:"Prof.",name:"Antonio",surname:"Bulbena",slug:"antonio-bulbena",fullName:"Antonio Bulbena"},{id:"174182",title:"Dr.",name:"Juan",surname:"Castaño",slug:"juan-castano",fullName:"Juan Castaño"},{id:"174191",title:"Dr.",name:"Cristina",surname:"Vendrell",slug:"cristina-vendrell",fullName:"Cristina Vendrell"},{id:"174192",title:"Dr.",name:"Maria Del Mar",surname:"Ballester",slug:"maria-del-mar-ballester",fullName:"Maria Del Mar Ballester"},{id:"174193",title:"Mrs.",name:"Silvia",surname:"Rosado",slug:"silvia-rosado",fullName:"Silvia Rosado"},{id:"174194",title:"Dr.",name:"Núria",surname:"Mallorquí",slug:"nuria-mallorqui",fullName:"Núria Mallorquí"},{id:"174195",title:"Dr.",name:"Carolina",surname:"Baeza",slug:"carolina-baeza",fullName:"Carolina Baeza"}],corrections:null},{id:"48395",title:"Anxiety in Natural and Surgical Menopause — Physiologic and Therapeutic Bases",doi:"10.5772/60621",slug:"anxiety-in-natural-and-surgical-menopause-physiologic-and-therapeutic-bases",totalDownloads:1999,totalCrossrefCites:7,totalDimensionsCites:13,hasAltmetrics:0,abstract:"Generalized anxiety disorder is one of the most common psychiatric disorders, affecting a high percentage of human beings around the world. This emotional disorder possesses marked gender differences and occurs more often in women than in men, in a proportion of 2:1. Accompanying the reproductive cycle of women are significant fluctuations in plasma and brain steroid hormone concentrations, including oestradiol, progesterone, and allopregnanolone, among others. These hormonal changes are related to some illnesses and with the development of anxiety and mood swings occurring in the premenstrual and postpartum period, and particularly during the menopause. Menopause is a clinical term used to indicate the cessation of the woman's reproductive ability that occurs naturally, but also may be surgically induced by bilateral oophorectomy, with or without the removal of the Fallopian tubes and uterus. Natural menopause includes specific periods related to the physiological and hormonal changes produced by ovarian failure, it is usually a natural stage that occurs to women in midlife, during their late 40s or early 50s, indicating the end of the reproductive period in the woman. During the menopause transition years, women experience changes in the production of ovarian hormones, which are associated with significant changes in the physiological, emotional, and affective processes. Unfortunately, surgical menopause occurs at an early age, and produces similar physiological and psychiatric disorders, but they are more severe in this instance. In both cases, typical symptoms associated with menopause critically deteriorate the mental health of the women. In this way, the therapeutic management of clinical symptoms of menopause include replacement hormone therapy, the use of anxiolytic and antidepressant drugs, and other natural alternatives based on the use of chemical compounds obtained from plants such as soya. However, a general effective treatment for menopause symptoms does not yet exist. For this reason, experimental studies have proposed ovariectomy in rats as a potential tool to study the effects of a long-term absence of ovarian hormones associated with surgical menopause, which also allowed the study of substances with potential therapeutic application to ameliorate typical symptoms associated with surgical menopause. The aim of this chapter is to review the participation of ovarian hormones in the regulation of emotional and affective disorders in women with natural or surgical menopause; particularly their anatomical pathways, neurotransmission systems, and the resulting behavioural patterns. Finally, preclinical and clinical research suggested that long-term absence of ovarian hormones associated with natural or surgical menopause is the principal cause of physiological and psychiatric disorder in the women; therefore, oestrogenic compounds seem to play an important role in the maintenance of the brain structures that regulate anxiety, mood, memory, and cognitive functions in menopausal women.",signatures:"Juan Francisco Rodríguez-Landa, Abraham Puga-Olguín, León Jesús\nGermán-Ponciano, Rosa-Isela García-Ríos and Cesar Soria-Fregozo",downloadPdfUrl:"/chapter/pdf-download/48395",previewPdfUrl:"/chapter/pdf-preview/48395",authors:[{id:"45702",title:"Dr.",name:"Juan Francisco",surname:"Rodríguez-Landa",slug:"juan-francisco-rodriguez-landa",fullName:"Juan Francisco Rodríguez-Landa"},{id:"174651",title:"Dr.",name:"Abraham",surname:"Puga-Olguín",slug:"abraham-puga-olguin",fullName:"Abraham Puga-Olguín"},{id:"174652",title:"MSc.",name:"León Jesús",surname:"Germán-Ponciano",slug:"leon-jesus-german-ponciano",fullName:"León Jesús Germán-Ponciano"},{id:"174653",title:"Dr.",name:"Rosa Isela",surname:"García-Ríos",slug:"rosa-isela-garcia-rios",fullName:"Rosa Isela García-Ríos"},{id:"174654",title:"Dr.",name:"Cesar",surname:"Soria-Fregozo",slug:"cesar-soria-fregozo",fullName:"Cesar Soria-Fregozo"}],corrections:null},{id:"48578",title:"A Systematic Review of Anxiety Disorders following Mild, Moderate and Severe TBI in Children and Adolescents",doi:"10.5772/60426",slug:"a-systematic-review-of-anxiety-disorders-following-mild-moderate-and-severe-tbi-in-children-and-adol",totalDownloads:1562,totalCrossrefCites:9,totalDimensionsCites:15,hasAltmetrics:0,abstract:"The aim of this chapter is to systematically review the research exploring the relationship between TBI and anxiety disorders in children and adolescents. A literature search was conducted using Google Scholar, Ovid Medline (1946 - Dec 2013), PsycINFO (1806 - Dec 2013), CINAHL plus (1937 - Dec 2013), Cochrane database (2005 – Dec 2013) and Embase (1946 – Dec 2013). The search returned 346 articles, and 11 of these met the inclusion criteria. Anxiety disorders were often found to be a negative outcome following childhood TBI, with a higher incidence of disorders including GAD, ASD, PTSD, PD, OCD, simple/specific phobia, social phobia and SAD found in children following their injury. In most cases, this relationship was strongest for children with severe TBI who sustained their injury at a younger age. Psychosocial adversity was found to be a consistently significant predictor for the likelihood of children developing anxiety following TBI. It is concluded that children who have suffered from a TBI (mild, moderate or severe), are at a higher risk of developing subsequent anxiety disorders, even 1 year following the injury event, and children with more severe injuries, greater psychosocial adversity, and younger age at injury are considered to be the most vulnerable.",signatures:"Michelle Albicini and Audrey McKinlay",downloadPdfUrl:"/chapter/pdf-download/48578",previewPdfUrl:"/chapter/pdf-preview/48578",authors:[{id:"169771",title:"BSc.",name:"Michelle",surname:"Albicini",slug:"michelle-albicini",fullName:"Michelle Albicini"},{id:"169982",title:"Dr.",name:"Audrey",surname:"McKinlay",slug:"audrey-mckinlay",fullName:"Audrey McKinlay"}],corrections:null},{id:"48585",title:"Impact of Anxiety and Depression Symptoms on Scholar Performance in High School and University Students",doi:"10.5772/60711",slug:"impact-of-anxiety-and-depression-symptoms-on-scholar-performance-in-high-school-and-university-stude",totalDownloads:4417,totalCrossrefCites:17,totalDimensionsCites:20,hasAltmetrics:1,abstract:"Emotional processes are important to survive. The Darwinian adaptive concept of stress refers to natural selection since evolved individuals have acquired effective strategies to adapt to the environment and to unavoidable changes. If demands are abrupt and intense, there might be insufficient time to successful responses. Usually, stress produces a cognitive or perceptual evaluation (emotional memory) which motivates to make a plan, to take a decision and to perform an action to face successfully the demand. Between several kinds of stresses, there are psychosocial and emotional stresses with cultural, social and political influences. The cultural changes have modified the way in which individuals socially interact. Deficits in familiar relationships and social isolation alter physical and mental health in young students, producing reduction of their capacities of facing stressors in school. Adolescence is characterized by significant physiological, anatomical, and psychological changes in boys and girls, who become vulnerable to psychiatric disorders. In particular for young adult students, anxiety and depression symptoms could interfere in their academic performance. In this chapter, we reviewed approaches to the study of anxiety and depression symptoms related with the academic performance in adolescent and graduate students. Results from available published studies in academic journals are reviewed to discuss the importance to detect information about academic performance, which leads to discover in many cases the very commonly subdiagnosed psychiatric disorders in adolescents, that is, anxiety and depression. With the reviewed evidence of how anxiety and depression in young adult students may alter their main activity in life (studying and academic performance), we discussed data in order to show a way in which professionals involved in schools could support students and stablish a routine of intervention in any case.",signatures:"Blandina Bernal-Morales, Juan Francisco Rodríguez-Landa and\nFrank Pulido-Criollo",downloadPdfUrl:"/chapter/pdf-download/48585",previewPdfUrl:"/chapter/pdf-preview/48585",authors:[{id:"45702",title:"Dr.",name:"Juan Francisco",surname:"Rodríguez-Landa",slug:"juan-francisco-rodriguez-landa",fullName:"Juan Francisco Rodríguez-Landa"},{id:"45701",title:"Dr.",name:"Blandina",surname:"Bernal-Morales",slug:"blandina-bernal-morales",fullName:"Blandina Bernal-Morales"},{id:"175891",title:"MSc.",name:"Frank",surname:"Pulido-Criollo",slug:"frank-pulido-criollo",fullName:"Frank Pulido-Criollo"}],corrections:null},{id:"48481",title:"The Role of Expectations in Treatment Outcome and Symptom Development in Anxiety Disorders",doi:"10.5772/60668",slug:"the-role-of-expectations-in-treatment-outcome-and-symptom-development-in-anxiety-disorders",totalDownloads:2078,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:0,abstract:"For more than 60 years, researchers have been interested in determining the impact of expectations on treatment outcome. Earlier studies mostly focused on two types of expectations: prognostic and process expectations. Aims: To review how four different types of expectations (prognostic, process, anxiety expectancy and anxiety sensitivity) contribute to psychotherapy outcome, and to the development of clinical disorders, especially anxiety. Conclusions: First, the role of process and prognostic expectancies in clinical disorders and psychotherapy outcome should be clarified by addressing the methodological flaws of the earlier expectancy studies. Second, studies, especially those on anxiety disorders, may benefit from evaluating the four different types of expectations to determine their relative impact on outcome, and on the development and maintenance of these disorders. Third, possible links with other clinical disorders should be further explored. Finally, expectancies should be assessed prior to treatment and after several sessions to determine the extent to which the treatment's failure in modifying initial low expectancies contribute to a poor outcome.",signatures:"Theodora E. Katerelos, Claude Bélanger, Marie-Christine Payette,\nGhassan El-Baalbaki, André Marchand and Michel Perreault",downloadPdfUrl:"/chapter/pdf-download/48481",previewPdfUrl:"/chapter/pdf-preview/48481",authors:[{id:"57536",title:"Prof.",name:"Claude",surname:"Belanger",slug:"claude-belanger",fullName:"Claude Belanger"},{id:"57551",title:"Dr.",name:"Michel",surname:"Perreault",slug:"michel-perreault",fullName:"Michel Perreault"},{id:"157412",title:"Prof.",name:"Ghassan",surname:"El-Baalbaki",slug:"ghassan-el-baalbaki",fullName:"Ghassan El-Baalbaki"},{id:"174810",title:"Dr.",name:"Theodora",surname:"Katerelos",slug:"theodora-katerelos",fullName:"Theodora Katerelos"},{id:"175625",title:"MSc.",name:"Marie-Christine",surname:"Payette",slug:"marie-christine-payette",fullName:"Marie-Christine Payette"},{id:"175626",title:"Prof.",name:"André",surname:"Marchand",slug:"andre-marchand",fullName:"André Marchand"}],corrections:null},{id:"48868",title:"Practical Applications of Complementary and Alternative Therapies in Adults and Youth with Anxiety Disorders",doi:"10.5772/61046",slug:"practical-applications-of-complementary-and-alternative-therapies-in-adults-and-youth-with-anxiety-d",totalDownloads:1563,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The use of complementary and alternative medicine (CAM) therapies is becoming an increasingly popular treatment option for anxiety disorders in adults and youth. Parents often are reluctant to use antidepressants due to fear of potential side effects. Youth are often unwilling to participate in cognitive behaviour therapy (CBT) or it is not readily available. Practitioners are not always knowledgeable or open to considering CAM therapies for their patients. This chapter will review the definition of CAM, prevalence of CAM use, its safety and effectiveness and finally its practical applications. Four case studies are discussed in which practitioners were open to the use of CAM and collaborated successfully with parents of youth with anxiety disorders. Literature review is presented for use of vitamin D, melatonin, acupuncture, acupressure and craniosacral therapy.",signatures:"Sarosh Khalid-Khan, Faiza Khalid-Khan and David Gratzer",downloadPdfUrl:"/chapter/pdf-download/48868",previewPdfUrl:"/chapter/pdf-preview/48868",authors:[{id:"174558",title:"Dr.",name:"Sarosh",surname:"Khalid-Khan",slug:"sarosh-khalid-khan",fullName:"Sarosh Khalid-Khan"}],corrections:null},{id:"48492",title:"Yoga for Anxiety Management in the Workplace",doi:"10.5772/60633",slug:"yoga-for-anxiety-management-in-the-workplace",totalDownloads:1848,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:'Anxiety is a potential cause of serious influence in a person\'s daily life. This chapter is focused specifically on the effects of the work environment factors in disease development and progression. Working environments are places of significant sources of stress: they put us in connection with our duties and expectations (often not coincide with reality) and with a "forced socialization." Especially forced socialization is to be considered a main source of stress in work places, inducing negative outcomes in work and social relationships. The psychosocial risks of work places can so be defined according to design, organization and management of work as well as to social and environmental contexts that may lead to damage of physical, social and psychological domains. Stress can then be defined as a pattern of emotional, cognitive, behavioral, and physiological reactions to adverse and noxious aspects perceived as the contents of the working environment. Thanks to the beginning of psychosocial research on the working environment, the impact of some aspects of the work environment on health has become an object of study, as the interventions to mitigate it. Yoga, meditation, and mantra techniques, as described in this work (presenting others’ experiences and two of own current projects, used in a hospital workplace to control the condition of occupational stress but also in a clinical sample), showed the potential in considerably reducing anxiety and mood symptoms.',signatures:"S. Doria, F. Irtelli, R. Sanlorenzo and F. Durbano",downloadPdfUrl:"/chapter/pdf-download/48492",previewPdfUrl:"/chapter/pdf-preview/48492",authors:[{id:"157077",title:"Dr.",name:"Federico",surname:"Durbano",slug:"federico-durbano",fullName:"Federico Durbano"},{id:"174641",title:"Dr.",name:"Floriana",surname:"Irtelli",slug:"floriana-irtelli",fullName:"Floriana Irtelli"},{id:"174682",title:"Dr.",name:"Stefania",surname:"Doria",slug:"stefania-doria",fullName:"Stefania Doria"},{id:"174683",title:"Dr.",name:"Roberto",surname:"Sanlorenzo",slug:"roberto-sanlorenzo",fullName:"Roberto Sanlorenzo"}],corrections:null},{id:"48516",title:"Mindfulness — New Research and Treatment Directions in Post-traumatic Stress Disorder",doi:"10.5772/60635",slug:"mindfulness-new-research-and-treatment-directions-in-post-traumatic-stress-disorder",totalDownloads:1720,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Combining mindfulness to strengthen emotion regulation with existing empirically supported post-traumatic stress disorder (PTSD) treatments may improve outcomes through increasing (a) engagement, (b) compliance, and (c) decreasing the level of ruminations in PTSD. Several psychotherapeutic interventions incorporating training in mindfulness are clinically relevant to traumatic stress. In order to see how far PTSD treatment could benefit from including mindfulness into the therapeutic process, we analyzed researches regarding: (a) the neuroscience of mindfulness, (b) assessment instruments for mindfulness, (c) mechanism of mindfulness, respective, and (d) the relation between mindfulness and other techniques. Based on this analysis, we can conclude that mindfulness may improve the therapeutic results and the outcome of PTSD patients. Mindfulness can be used in two ways: (a) as an emotion regulation support technique for existing empirically supported PTSD treatments and (b) as a standalone treatment- mindfulness-based cognitive behavior psychotherapies.",signatures:"Delia M. Podea and Karol J. Wild",downloadPdfUrl:"/chapter/pdf-download/48516",previewPdfUrl:"/chapter/pdf-preview/48516",authors:[{id:"30327",title:"Prof.",name:"Delia",surname:"Podea",slug:"delia-podea",fullName:"Delia Podea"},{id:"174602",title:"Ms.",name:"Karol Julien",surname:"Wild",slug:"karol-julien-wild",fullName:"Karol Julien Wild"}],corrections:null},{id:"49059",title:"Anxiety — Is There an App for That? Considering Technology, Psychiatry, and Internet-Assisted Cognitive Behavioral Therapy",doi:"10.5772/60816",slug:"anxiety-is-there-an-app-for-that-considering-technology-psychiatry-and-internet-assisted-cognitive-b",totalDownloads:1565,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Across Western countries, more than a third of people will have a mental health disorder over their lifetime; mood and anxiety disorders are the most common. The effectiveness of psychological interventions is well established. Cognitive Behavioural Therapy (CBT), for example, is as effective for mild and moderate anxiety as medications; combined psychopharmacology and CBT is superior to either modality alone, suggesting a synergistic effect. However, CBT requires a major investment of time and resources. Thus, in public systems, CBT has limited availability and is subject to long waiting times; primary-care physicians and psychiatrists may not offer CBT.",signatures:"David Gratzer, Faiza Khalid-Khan and Sarosh Khalid-Khan",downloadPdfUrl:"/chapter/pdf-download/49059",previewPdfUrl:"/chapter/pdf-preview/49059",authors:[{id:"174558",title:"Dr.",name:"Sarosh",surname:"Khalid-Khan",slug:"sarosh-khalid-khan",fullName:"Sarosh Khalid-Khan"},{id:"174958",title:"Dr.",name:"David",surname:"Gratzer",slug:"david-gratzer",fullName:"David Gratzer"},{id:"174996",title:"Ms.",name:"Faiza",surname:"Khalid-Khan",slug:"faiza-khalid-khan",fullName:"Faiza Khalid-Khan"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6493",title:"Psychotic Disorders",subtitle:"An Update",isOpenForSubmission:!1,hash:"10c61c9adb13a7f0780176f556353b2e",slug:"psychotic-disorders-an-update",bookSignature:"Federico Durbano",coverURL:"https://cdn.intechopen.com/books/images_new/6493.jpg",editedByType:"Edited by",editors:[{id:"157077",title:"Dr.",name:"Federico",surname:"Durbano",slug:"federico-durbano",fullName:"Federico Durbano"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3295",title:"New Insights into Anxiety Disorders",subtitle:null,isOpenForSubmission:!1,hash:"013c40f1052a8d6b3ab2d6bb6f797921",slug:"new-insights-into-anxiety-disorders",bookSignature:"Federico Durbano",coverURL:"https://cdn.intechopen.com/books/images_new/3295.jpg",editedByType:"Edited by",editors:[{id:"157077",title:"Dr.",name:"Federico",surname:"Durbano",slug:"federico-durbano",fullName:"Federico Durbano"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5384",title:"New Developments in Anxiety Disorders",subtitle:null,isOpenForSubmission:!1,hash:"c355c8b1bce4b291f916878f12e0ab8a",slug:"new-developments-in-anxiety-disorders",bookSignature:"Federico Durbano and Barbara Marchesi",coverURL:"https://cdn.intechopen.com/books/images_new/5384.jpg",editedByType:"Edited by",editors:[{id:"157077",title:"Dr.",name:"Federico",surname:"Durbano",slug:"federico-durbano",fullName:"Federico Durbano"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5795",title:"Psychopathy",subtitle:"New Updates on an Old Phenomenon",isOpenForSubmission:!1,hash:"2e33769ff3c930094c4e350ec6a2b6b5",slug:"psychopathy-new-updates-on-an-old-phenomenon",bookSignature:"Federico Durbano",coverURL:"https://cdn.intechopen.com/books/images_new/5795.jpg",editedByType:"Edited by",editors:[{id:"157077",title:"Dr.",name:"Federico",surname:"Durbano",slug:"federico-durbano",fullName:"Federico Durbano"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"510",title:"Anxiety Disorders",subtitle:null,isOpenForSubmission:!1,hash:"183445801a9be3bfbce31fe9752ad3db",slug:"anxiety-disorders",bookSignature:"Vladimir Kalinin",coverURL:"https://cdn.intechopen.com/books/images_new/510.jpg",editedByType:"Edited by",editors:[{id:"31572",title:null,name:"Vladimir V.",surname:"Kalinin",slug:"vladimir-v.-kalinin",fullName:"Vladimir V. 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\r\n\tThe surgical global carefulness and charge require not only the training in the basic sciences, the knowledge of pathology, the application of technical skills, and the management of diagnostic and therapeutic problems. The surgical activity should be completed by a deep understanding of the patient and the commitment to ensure a rapid return to normal after surgical treatment. The surgeon’s initial contact with the patient is important. That is the opportunity to gain the patient’s confidence and convey the assurance that the therapeutic procedure is available and effective. The surgeon needs to demonstrate concern and commitment for the patient as a person who requires care and not just a “case” to be treated by surgical therapy. Today we must believe that the rapid and safe recovery after surgical therapy is a fundamental feature alongside the results of resolution/control of the pathology. The first revolutionary step in this perspective has been taken, and for thirty years it has continued and evolved universally, through the introduction of minimally invasive therapeutic approaches. The pathway of control and improvement of perioperative care have had great diffusion, in the last years, in the current practice of surgical wards. The perioperative procedure scheme that applies the principle of a safe and rapid postoperative recovery is standardized in the system unanimously called Enhanced Recovery After Surgery (ERAS). The basic characteristics of the procedure are the enterprise to improve surgical quality, the choice of innovative management and therapeutic procedures, but always based on evidence, and finally the multidisciplinarity that involves medical skills from various medical specialties in the management of treatments. Therefore all surgical specialties are involved in what we can define as a cultural movement in medical practice. The particular aspects of improving recovery following surgical procedures are summarized in the following actions: to support patient mobilization, decrease complication rates after surgery, shorten the length of hospital stay and reduce costs. The components of enhanced recovery after surgery are varied and numerous and can be divided in detail into the pre, intra, and postoperative phases. Among other elements are included: smoking and alcohol use stopped some weeks and nutritional assessment before surgery, intraoperative use of antibiotics, postoperative multimodal analgesia and nausea control, active and early mobilization, early feeding, reduce as much as possible the use of the nasogastric tube and abdominal drains, early removal of a urinary catheter. Finally, two propositions are in evidence. a multidisciplinary medical team should be implicated in achieving the accomplishment of an ERAS pathway; moreover, the main purposes of ERAS are to reduce the surgical stress and to preserve, how is it possible, the physiological conditions, consequently these therapeutic choices should, in principle. be employed routine, currently in surgery.
",isbn:"978-1-80356-762-4",printIsbn:"978-1-80356-761-7",pdfIsbn:"978-1-80356-763-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"134f0a1f56e2a14a8328a8b1221523ec",bookSignature:"Prof. Vincenzo Neri",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11869.jpg",keywords:"Bowel Preparation, Anemia Control Before Surgery, ERAS, Preoperative Counseling, Intraoperative Antibiotics, Minimally Invasive Approach, Enhanced Recovery Pathways, Postoperative Analgesia, Mininvasive Approach, Health Care Team, Nutritional Status, Carbohydrate Loading",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 25th 2022",dateEndSecondStepPublish:"April 22nd 2022",dateEndThirdStepPublish:"June 21st 2022",dateEndFourthStepPublish:"September 9th 2022",dateEndFifthStepPublish:"November 8th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"4 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Neri is the author of more than 330 scientific papers edited in national and international journals and chapters of books. He obtained the certificate of “Maitrise Universitaire en Pedagogie des Sciences de la Santè” on the Universitè Paris – Nord Bobigny. Dr. Neri is a member of scientific societies: SIC, IHPBA, AISP, EASL, NESA, SLS.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"170938",title:"Prof.",name:"Vincenzo",middleName:null,surname:"Neri",slug:"vincenzo-neri",fullName:"Vincenzo Neri",profilePictureURL:"https://mts.intechopen.com/storage/users/170938/images/system/170938.jpeg",biography:"Vincenzo Neri is a former Professor of General Surgery (retired), Department of Medical and Surgical Sciences, University of Foggia, Italy. He also held positions such as Director of Division of General Surgery, Director of Residency School of General Surgery, Director of Department of Surgical Sciences, and President of Course of Degree of Medicine and Surgery at the same university. He also served as an assistant professor (1974–1982) and associate professor (1982–2001) at the School of Medicine and Surgery, University of Bari, Italy, where he obtained a degree in Medicine and Surgery and completed postgraduate training in General Surgery and Emergency Surgery. He obtained a diploma of 'Maitrise Universitaire en Pedagogie des Sciences de la Santè” from the University Paris-Nord Bobigny in 1995. Dr. Neri’s research interests include hepatobiliary pancreatic surgery, acute pancreatitis, and treatment of pancreatic and liver tumors. He has published research papers, reviews, congress proceedings, and book chapters. In the period 1991–2016, he attended the Hepatobiliarypancreatic Surgery Service of Beaujon Hospital, Universitè de Paris, Clichy. As part of the 2010–2011 ERASMUS Program, Dr. Neri developed a seminar on 'Cystic Tumours of the Pancreas” at Ghent University, Belgium. He is a member of several scientific associations including Società Italiana di Chirurgia (SIC), International Hepato-Pancreato Biliary Association (IHPBA), European Association for the Study of the Liver (EASL), New European Surgical Academy (NESA), and Society of Laparoscopic and Robotic Surgeons (SLS).",institutionString:"University of Foggia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"9",totalChapterViews:"0",totalEditedBooks:"5",institution:{name:"University of Foggia",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"455410",firstName:"Dajana",lastName:"Jusic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/455410/images/20500_n.jpeg",email:"dajana.j@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"21844",title:"Thermodynamics of Ligand-Protein Interactions: Implications for Molecular Design",doi:"10.5772/19447",slug:"thermodynamics-of-ligand-protein-interactions-implications-for-molecular-design",body:'\n\t\t
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1. Introduction
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Biologically relevant macromolecules, such as proteins, do not operate as static, isolated entities. On the contrary, they are involved in numerous interactions with other species, such as proteins, nucleic acid, membranes, small molecule ligands, and also, critically, solvent molecules. These interactions often display a remarkable degree of specificity and high affinity. Fundamentally, the biological processes rely on molecular organisation and recognition events. Binding between two interacting partners has both enthalpic (ΔH) and entropic (-TΔS) components, which means the recognition event is associated with changes of both the structure and dynamics of each counterpart. Like any other spontaneous process, binding occurs only when it is associated with a negative Gibbs\' free energy of binding (\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t), which may have differing thermodynamic signatures, varying from enthalpy- to entropy-driven. Thus, the understanding of the forces driving the recognition and interaction require a detailed description of the binding thermodynamics, and a correlation of the thermodynamic parameters with the structures of interacting partners. Such an understanding of the nature of the recognition phenomena is of a great importance for medicinal chemistry and material research, since it enables truly rational structure-based molecular design.
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This chapter is organised in the following way. The first part of it introduces general principles which govern macromolecular associations under equilibrium conditions: the free energy of binding and its enthalpic and entropic components, the contributions from both interacting partners, interaction energy of the association, and specific types of interactions – such as hydrogen bonding or van der Waals interactions, ligand and protein flexibility, and ultimately solvent effects (e.g. solute-solvent interactions, solvent reorganisation). The second part is dedicated to methods applied to assess particular contributions, experimental as well as computational. Specifically, there will be a focus on isothermal titrational calorimetry (ITC), solution nuclear magnetic resonance (NMR), and a discussion of computational approaches to the estimation of enthalpic and entropic contributions to the binding free energy. I will discuss the applicability of these methods, the approximations behind them, and their limitations. In the third part of this chapter, I will provide the reader several examples of ligand-protein interactions and focus on the forces driving the associations, which can be very different from case to case. Finally, I will address several practical aspects of assessing the thermodynamic parameters in molecular design, the bottlenecks of methods employed in such process, and the directions for future development.
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The information content provided by thermodynamic parameters is vast. It plays a prominent role in the elucidation of the molecular mechanism of the binding phenomenon, and – through the link to structural data – enables the establishment of the structure-activity relationships, which may eventually lead to rational design. However, the deconvolution of the thermodynamic data and particular contributions is not a straightforward process; in particular, assessing the entropic contributions is often very challenging.
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Two groups of computational methods, which are particularly useful in assessment of the thermodynamics of molecular recognition events, will be discussed. One of them are methods based on molecular dynamics (MD) simulations, provide detailed insights into the nature of ligand-protein interactions by representing the interacting species as a conformational ensemble that follows the laws of statistical thermodynamics. As such, these are very valuable tools in the assessment of the dynamics of such complexes on short (typically, picosecond to tens of nanosecond, occasionally microsecond) time scales. I will give an overview of free energy perturbation (FEP) methods, thermodynamic integration (TI), and enhanced sampling techniques. The second group of computational methods relies on very accurate determinations of energies of the macromolecular systems studied, employing calculations based on approximate solutions of the Schrödinger equation. The spectrum of these quantum chemical (QM) methods applied to study ligand-protein interactions is vast, containing high-level ab initio calculations: from Hartree-Fock, through perturbational calculations, to coupled-clusters methods; DFT and methods based on it (including “frozen” DFT and SCC-DFTTB tight binding approaches); to semi-empirical Hamiltonians (such as AM1, PM3, PM6, just to mention the most popular ones) (Piela, 2007, Stewart, 2009). Computational schemes based the hybrid quantum mechanical –molecular mechanical (QM/MM) regimes will also be introduced. Due to the strong dependence of the molecular dynamics simulations on the applied force field, and due to the dependence of both MD simulations and QM calculations on the correct structure of the complex, validation of results obtained by these methodologies against experimental data is crucial.
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Isothermal titration calorimetry (ITC) is one of the techniques commonly used in such validations. This technique allows for the direct measurement of all components of the Gibbs\' equation simultaneously, at a given temperature, thus obtaining information on all the components of free binding energy during a single experiment. Yet since these are de facto global parameters, the decomposition of the factors driving the association, and investigation of the origin of force that drives the binding is usually of limited value. Nonetheless, the ITC remains the primary tool for description of the thermodynamics of ligand-protein binding (Perozzo et al., 2004). In this chapter, I will give a brief overview of ITC and its applicability in the description of recognition events and to molecular design.
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Another experimental technique, which has proven very useful in the experimental validation of computational results, is NMR relaxation. These measurements are extremely valuable, as they specifically investigate protein dynamics on the same time scales as MD simulations. As such, the results obtained can be directly compared with simulation outputs. In addition, the Lipari-Szabo model-free formalism (Lipari and Szabo, 1982) is relatively free of assumptions regarding the physical model describing the molecular motions. The only requirement is the internal dynamics being uncorrelated with the global tumbling of the system under investigation. The results of the Lipari–Szabo analysis, in the form of generalised order parameters (\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tL\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t), can be readily interpreted in terms of the conformational entropy associated with the measured motions (Yang and Kay, 1996). It has been shown that for a wide range of motion models, the functional dependence of the conformational entropy on the order parameter is similar, suggesting that changes in order parameters can be related to changes in entropy in a model-independent manner. I will introduce the application of this model-free formalism to MD simulation, for the study of dynamical behaviour of ligand-protein complexes and the estimation of changes in the conformational entropy upon ligand-protein association. The MD simulations, performed on several proteins in complexes with their cognate ligands, indicate that the molecular ensembles provide a picture of the protein backbone dynamics that show a remarkably high degree of consistency with NMR relaxation data, regardless of the protein\'s size and structure (Schowalter and Brüschweiler, 2007).
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In this chapter I will also address the enthalpy-entropy compensation phenomenon and the challenges it imposes on molecular design. The generality of this phenomenon have been a subject of debate for many years. Although this compensation is not a thermodynamic requirement as such (Ford, 2005, Sharp, 2001), it has been very frequently observed in protein-ligand interactions (Whitesides and Krishnamurthy, 2005). Briefly, stronger and more directed interactions are less entropically favourable, since the tight binding constricts molecular motions. The detailed mechanism of enthalpy-entropy compensation is, nonetheless, highly system-dependent, and this compensation does not obey a single functional form. An example of enthalpy-entropy compensation and its consequences to the design process will be provided.
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A discussion of the thermodynamics of protein-ligand interactions would not be complete without commenting on dynamic allostery and cooperativity. The mechanism of allostery plays a prominent role in control of protein biological activity, and it is becoming accepted that protein conformational dynamics play an important role in allosteric function. Changes of protein flexibility upon ligand binding affect the entropic cost of binding at distant protein regions. Counter-intuitively, proteins can increase their conformational entropy upon ligand binding, thus reducing the entropic cost of the binding event (MacRaild et al., 2007). I will discuss these phenomena, illustrating them through several examples of biologically-relevant protein-ligand interactions.
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The overall aim of this chapter is to introduce the forces driving binding events, and to make the reader familiar with some general rules governing molecular recognition processes and equally to raise awareness of the limitations of these rules. Combining the structural information with equilibrium thermodynamic data does not yield an understanding of the binding energetics under non-equilibrium conditions, and global parameters, obtained during ITC experiments, do not enable us to assess the individual contributions to the binding free energy. Certain contributions, such as entropy, may behave in a strongly non-additive and highly correlated manner (Dill, 1997). This chapter will discuss the boundaries of rational molecular design guided by thermodynamic data.
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2. Principles
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2.1. Enthalpic and entropic components of free binding energy
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A non-covalent association of two macromolecules is governed by general thermodynamics. Similarly to any other binding event (or – in a broader context – to any spontaneous process), it occurs only when it is coupled with a negative Gibbs\' binding free energy (1), which is the sum of an enthalpic, and an entropic, terms:
where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is free binding energy, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tis enthalpy, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tentropy, and T is the temperature.
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The enthalpic contribution to the free energy reflects the specificity and strength of the interactions between both partners. These include ionic, halogen, and hydrogen bonds, electrostatic (Coulomb) and van der Waals interactions, and polarisation of the interacting groups, among others. The simplest description of entropic contribution is that it is a measure of dynamics of the overall system. Changes in the binding entropy reflect loss of motion caused by changes in translational and rotational degrees of freedom of the interacting partners. On the other hand, changes in conformational entropy may be favourable and in some cases these may reduce the entropic cost of binding (MacRaild et al., 2007). Solvation effects, such as solvent re-organisation, or the release of tightly bound water upon ligand binding can contribute significantly to the entropic term of the binding free energy.
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The Gibbs equation can be also written as in equation (2):
where R is a gas constant, T is the temperature, and \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tK\n\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tis binding constant. This formulation emphasises the relationship between Gibbs energy and binding affinity. The ligand-protein association process can be represented in the form of a Born-Haber cycle. A typical cycle is showed in Figure 1. The \'intrinsic\' free energy of binding between ligand L and protein P is represented by\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, whereas the experimentally observable free energy of binding is represented by\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\to\n\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t.
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Figure 1.
An example of Born-Haber cycle for ligand-protein (LP) association. It relates the experimentally observed free energy of binding (\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\to\n\t\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t) with \'intrinsic\' free energy of binding (\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t) between ligand (L) and protein (P) and with solvation free energies of free interactors (\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\t\t\tf\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t) and the resulting complex (\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t). X, Y, Z, and B refer to the number of water molecules involved in solvation of the unbound ligand (X), unbound protein (Y), ligand-protein complex (Z), and to the bulk solvent (B).
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Two additional processes can be defined: the free energy of solvation of the free (unbound) interacting partners (\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\tf\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t), and the free energy of solvation of the ligand-protein complex (\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t). Since the free energy is a state function, it is independent of the path leading from from one state of the system to another. Hence, the observable free energy of binding can be written as in equation (3):
The equation above shows how the observable free energy of binding can be decomposed into the \'intrinsic\' term, and the solvation contributions from the ligand-protein complex and unbound interactors. Similar decomposition can be done for the enthalpic and entropic terms separately, as these terms are also state functions.
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Since the enthalpic and entropic contributions to the binding free energy depend on many system-specific properties (such as protonation states, binding of metal cations, changes in conformational entropy from one ligand to another in a way which is very difficult to predict, etc), the conclusion is that optimising the overall free energy remains the most viable approach to rational (structure-based) molecular design. Attempting to get an insight into individual components of the free energy requires re-thinking the whole concept of ligand-protein binding. This means regarding ligand-protein complexes as specifically interacting yet flexible ensembles of structures rather than rigid entities, and the role of solvation effects. The significant contribution of specific interactions and flexibility to the \'intrinsic\' component of binding free energy, and solvation effects will be discussed next in this chapter.
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2.2. Specific interactions
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2.2.1. Electrostatic interactions
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Electrostatic interactions, involved in ligand-protein binding events, can be roughly classified into three types; charge-charge, charge-dipole, and dipole-dipole. Typical charge-charge interactions are those between oppositely charged atoms, ligand functional groups, or protein side chains, such as positively charged (amine or imine groups, lysine, arginine, histidine) and negatively charged (carboxyl group, phosphate groups, glutamate side chain). An important contribution to the enthalpy change associated with a binding event arises from charge-dipole interactions, which are the interactions between ionised amino acid side chains and the dipole of the ligand moiety or water molecule. The dipole moments of the polar side chains of amino acid also affect their interaction with ligands.
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2.2.2. Van der Waals interactions
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Van der Waals interactions are very important for the structure and interactions of biological molecules. There are both attractive and repulsive van der Waals interactions that control binding events. Attractive van der Waals interactions involve two induced dipoles that arise from fluctuations in the charge densities that occur between adjacent uncharged atoms, which are not covalently bound. Repulsive van der Waals interactions occur when the distance between two involved atoms becomes very small, but no dipoles are induced. In the latter case, the repulsion is a result of the electron-electron repulsion that occurs in two partly-overlapping electron clouds.
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Van der Waals interactions are very weak (0.1- 4 kJ/mol) compared to covalent bonds or electrostatic interactions. Yet the large number of these interactions that occur upon molecular recognition events makes their contribution to the total free energy significant.
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Van der Waals interactions are usually treated as a simple sum of pairwise interatomic interactions (Wang et al., 2004). Multi-atom VdW interactions are, in most cases, neglected. This follows the Axilrod-Teller theory, which predicts a dramatic (i.e. much stronger than for pairwise interactions) decrease of three-atom interactions with distance (Axilrod and Teller, 1943). Indeed, detailed calculations of single-atom liquids (Sadus, 1998) and solids (Donchev, 2006) indicate that multi-body effects amount to only 5% of the total energy (Finkelstein, 2007). However, Finkelstein (2010) shows that those largely ignored multi-atom Van der Waals interactions may lead to significant changes in free energy in the presence of covalent bonds. Those changes can be comparable to those caused by the substitutions of one atom by another one in conventional pairwise Van der Waals interactions. Thus, the currently used force fields (applied in MD simulations) need to be revised.
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2.2.3. Hydrogen bonds
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Hydrogen bonds are non-covalent, attractive interactions between a hydrogen covalently bonded to some electronegative group (“donor”), and another electronegative atom, such as oxygen or nitrogen (“acceptor”). The hydrogen bond can be described as an electrostatic dipole-dipole interaction. However, it also has some features of covalent bonding: it is specific, directional, it produces interatomic distances shorter than sum of van der Waals radii, and usually it involves a limited number of interaction partners, which can be interpreted as a type of valence.
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Proteins contain ample hydrogen bond donors and acceptors both in their backbone and in the side chains. The environment (aqueous solvent, protein-protein network, lipid bilayers) in which proteins of interest are immersed also contains numerous proton donors and acceptors – be it water molecule, interacting proteins, lipid headgroups, or DNA/RNA. Hydrogen bonding, therefore, occurs not only between ligand and protein and within the protein itself, but also within the surrounding medium.
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Like all non-covalent interactions, hydrogen bonds are fairly weak: in biological conditions, the strength of hydrogen bonds varies between 5-30 kJ/mol (outside of biological systems, the strength of hydrogen bonds may vary from 2 kJ/mol to even 155 kJ/mol for HF2-) (Emsley, 1980), which is weaker than ionic or covalent bonds. However, because of their relative weakness, they can be formed and broken rapidly during binding event, conformational changes, or protein folding. Thus, hydrogen bonds in biological systems may be switched on or off with energies that are within the range of thermal fluctuations. This is one of the prime factors that facilitates macromolecular association events, and biological activity. Another key factor is related to the strict geometric rules, followed by hydrogen bonds in biological systems. Namely, their orientations, lengths, and angular preferences, which make hydrogen bonding very specific. Due to these properties, the role of hydrogen bonds in governing specific interactions in biological recognition processes is absolutely crucial. Hydrogen bonds, both intra-and inter-molecular, are partly responsible for the secondary, tertiary, and quaternary structures of proteins, nucleic acids, and also some synthetic polymers. They play a pivotal role in molecular recognition events, and they tune the properties of the macromolecular system (e.g. mechanical strength, binding specificity). These geometric rules were among the first to be extracted from crystal structure databases (Bissantz et al., 2010). While the preferred geometries of hydrogen bonds are easily defined, their contributions to binding free energy are system-specific (Davis and Teague, 1999, Williams and Ladbury, 2003). Hydrogen bonds always convey specificity to a recognition process but do not always add much binding free energy (Bissantz et al., 2010).
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Hydrogen bonds can vary quite considerably in their strength. Often, a stronger hydrogen bond implies higher penalty of desolvation, so the net free energy gain of a stronger hydrogen bond might be seriously compromised. However, such a picture is not always the case. Hydrogen bond strength, in the context of the free energy changes, should be carefully examined, as it is likely to vary considerably from one ligand-protein system to another one (Barratt et al., 2005, 2006).
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Regarding weak hydrogen bonds, the most prominent donor is the CH group. These interactions, despite of their weakness, play an important role in stabilising appropriate conformations of ligand-protein complexes, for instance among the complexes between protein kinases and their inhibitors (Bissantz et al., 2010). Protonated histidines can also act as strong CH donors (Chakrabarti and Bhattacharyya, 2007). Weak hydrogen bonds, their nature, and their role in ligand-protein interactions have been extensively reviewed by Panigrahi and Desiraju (2007).
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2.2.4. Halogen bonds and multipolar interactions
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The concept of halogen bonds is similar to hydrogen bonds: both types of interactions involve relationships between an electron donor and electron acceptor. In hydrogen bonding, a hydrogen atom acts as the electron acceptor and forms a non-covalent bond by accepting electron density from an electronegative atom (“donor”). In halogen bonding, a halogen atom is the donor.
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Despite of their prevalence in complexes between proteins and small organic inhibitors (many of them contain halogen atoms due to solubility and bioavailability) and their importance for medicinal chemistry, the significance of halogen bonds in biological context has been overlooked for a long time (Zhou et al., 2010). For a number of years, halogen atoms were regarded as hydrophobic appendages, convenient – from the molecular design point of view - to fill apolar protein cavities. The nature of halogen interactions (such as directionality, sigma-holes) was not studied in detail and not regarded as very important. Indeed, halogen bonds are, in general, fairly weak interactions. On the other hand, in some cases they can compete with hydrogen bonds, thus should be considered in more details, given the importance of hydrogen bonds in ligand-protein interactions and given that many of synthesised small organic compounds contain halogen bonds in their structure (Bissantz et al., 2010, Zhou et al., 2010).
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Halogens involved in halogen bonds are chlorine, bromine, iodine, and fluorine (not very often). All four halogens are capable of acting as donors (as proven by computational and experimental data) and follow the general trend: F < Cl < Br < I, with iodine normally forming the strongest bonds, as the strength increases with the size of the halogen atom. From the chemical point of view, the halogens, with the exception of fluorine, have unique electronic properties when bound to aryl or electron withdrawing alkyl groups. They show an anisotropy of electron density distribution with a positive area (so-called σ-hole) of electrostatic potential opposite the carbon-halogen bond (Clark et al., 2007). The molecular origin of the σ-hole can be explained quantum chemically and the detailed description is provided in the work by Clark and coworkers (2007). Briefly, a patch of negative charge is formed around the central region of the bond between carbon and halogen atom, leaving the outermost region positive (hence the “hole”).
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Available experimental data show the strong influence of halogen bonds on binding affinity. Replacement of hydrogen by halogen atom is often used by medicinal chemists in order to increase the affinity. Indeed, in a series of adenosine kinase inhibitors, a 200-fold affinity gain from hydrogen to iodine has been observed (Iltzsch et al., 1995). Another spectacular, 300-fold affinity difference upon iodine substitution was observed in a series of HIV reverse-transcriptase inhibitors (Benjahad et al., 2003). Unsurprisingly, substitution of hydrogen by iodine typically leads to the largest affinity gain, since the strength of the halogen bond increases with the size of halogen atom.
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Halogen atoms can interact with the oxygen and with the carbon atoms of C=O groups, as well. The former attributes to the halogen bond formation, the latter is a hallmark of so-called orthogonal multipolar interactions. These interactions are formed by two dipolar functional groups, which are in a close distance from each other. Only recently it received attention in the field of medicinal chemistry and ligand-protein interactions (Paulini et al., 2005), even though it has been described for a long time. This interaction is known to contribute to ligand-protein stabilisation (Fischer et al., 2008), and it is particularly important in the context of halogen bonds (Bissantz et al., 2010 and references therein). It is worth bearing in mind that in an orthogonal (perpendicular) orientation of two dipoles, the actual dipole contribution to interaction energy is zero. Thus, higher order electrostatic and dispersion terms must be responsible for this type of interaction. The disappearance of the dipole term may turn a repulsive electrostatic interaction into an attractive one. Because of its high electron density and low polarisability, fluorine\'s preference for dipolar interactions is more pronounced than for the other halogens (Bissantz et al., 2010). Chlorine and other heavy halogens also form multipolar interactions with carbonyl groups, but they show a tendency for the C-X bond to be parallel rather than orthogonal to the amide plane, a consequence of the \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tσ\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t-hole (Bissantz et al., 2010).
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2.2.5. Hydrophobic interactions
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The interactions between ligands and the hydrophobic side chains of proteins contribute significantly to the binding free energy. The hydrophobic residues mutually repel water and other polar groups and results in a net attraction of the non-polar groups of ligand. In addition, apolar and aromatic rings of tryptophan, phenylalanine, and tyrosine participate in "stacking" interactions with aromatic moieties of ligand. Many studies have demonstrated that the hydrophobic interactions, quantified by the amount of hydrophobic surface buried upon ligand binding, is the structural parameter correlating best with binding free energy (Bissantz et al., 2010, Perozzo et al., 2004). It holds well for very diverse sets of ligands (Boehm and Klebe, 1996) as well as for protein-protein interactions (Vallone et al., 1998). It should be emphasised, though, that a considerable part of the affinity gain caused by hydrophobic interactions in hydrophobic binding pockets comes from sub-optimal solvation of the pocket in the unbound (apo) state.
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Aromatic interactions, hydrophobic effect, and other solvent effects will be discussed further in the following parts of this chapter.
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2.2.6. Interactions mediated by aromatic rings
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Aromatic rings deserve special attention in the context of ligand-protein interactions. Interactions between ligands and protein aromatic side chains ( Phe, Trp,and Tyr) are widespread in ligand-protein complexes (Bissantz et al., 2010). The unique steric and electronic properties of these side chains, which give rise to large polarizabilities and quadrupole moments, result in preferred geometries upon interactions.
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For interactions between two aromatic systems, two geometries are predominant: one, where two rings are parallel to each other, and the perpendicular, edge-to-face arrangement. High-accuracy ab initio CCSD(T) quantum chemical calculations of the dimerisation energy of benzene predict these two geometries to be isoenergetic (Hobza et al., 1996), which agrees with experimental results qualitatively and quantitatively (Grover et al., 1987, Krause et al., 1991).
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An introduction of heteroatoms into aromatic ring affects the ratio of both geometries. The preference to perpendicular interactions increases when the acidity of the interacting “side” atoms increases; this happens upon introduction of a strongly electron-withdrawing substituent in either ortho- or para-position. This was demonstrated by high-accuracy quantum chemical calculations by Sinnokrot and Sherrill (2004): The interaction between benzene as a donor and fluorobenzene as the acceptor, while both compounds were perpendicular to each other, was ~0.3 kcal/mol weaker than that of the benzene dimer. With reverse of roles (fluorobenzene as the donor), the interaction became ~0.6 kcal/mol stronger as compared to the benzene dimer.
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For perpendicularly-oriented aromatic-aromatic interactions, studies on several model systems showed that aliphatic-aromatic interactions in the same orientation provide a favourable contribution to the free energy of the same magnitude as aromatic-aromatic interactions (Turk and Smithrud, 2001). For aliphatic-aromatic interactions, interactions energy becomes more favourable when acidity of the interacting CH unit of aliphatic counterpart increases. Study conducted by Tsuzuki et al. (2000) showed that ethane (sp3 hybridisation of carbon atom, less acidic) is a worse binder of benzene than acetylene (sp hybridisation of carbon atom, more acidic), and the difference in dissociation energies between acetylene-benzene and ethane-benzene complexes is around 1 kcal/mol. In ligand-protein complexes, this type of interaction can be found in interactions between aromatic side chains and methyl groups. The strength of such interactions depends on the group to which the interacting methyl group is bound: the more electronegative the group, the more the preference towards perpendicular geometry of interacting methyl-aromatic side chain is pronounced (Bissantz et al., 2010). \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΠ\n\t\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t\tπ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tinteractions are also displayed by amide bonds of protein backbone (namely, their pi faces) and ion pairs - interactions between acidic (Asp, Glu) and basic (Lys, Arg) side chains.
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Aromatic interactions are not limited to \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tπ\n\t\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t\tπ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tinteractions. Recently, the nature of favourable interactions between heavier halogens and aromatic rings has been studied, in particular in the context of halogen bonds. C-H - halogen interactions can be regarded as “very weak hydrogen bonds” (Desiraju, 2002).
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2.3. Solvent effects, structural waters, and the bulk water
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Any binding event displaces water molecules from the interaction interface or from the binding pocket, while simultaneously desolvating the ligand (or a part of it). Although most of those waters are disordered and loosely associated with protein structure, such displacement affects the whole solvation shell around the ligand-protein complex (Poornima and Dean, 1995b).
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While the vast majority of those water molecules are mobile and easily displaceable, some are tightly bound to the protein structure. Tightly bound water molecules are often conserved across multiple crystal structures of ligand-protein complexes (Poornima and Dean, 1995c). Often, those water molecules play an important role in tuning the biological activity of the protein, as in the case of many enzymes (Langhorst et al., 1999, Nagendra et al., 1998, Poornima and Dean, 1995a). Those water molecules may be regarded as part of the protein structure. Ligand-protein interactions are often mediated by water molecules buried in the binding site and forming multiple hydrogen bonds with both binding partners (Poornima and Dean, 1995a-c). In other cases, those bound water molecules are released to the bulk upon ligand binding. Such displacement may affect the thermodynamic signature of the binding event in a dramatic way. It is generally assumed that the release of a water molecule from a rigid environment should be entropically favorable. The upper limit of the entropy gained for transferring a water molecule from a protein to bulk solvent was estimated to be 2 kcal/mol at room temperature (Dunitz, 1994). This gain would be compensated by loss of enthalpy, so the total contribution to the free energy (as a sum of its enthalpic and entropic terms) of a single water molecule released from the protein to the bulk is difficult to guess. Moreover, in order to reach this 2 kcal/mol limit the water molecule would have to be fixed very rigidly while bound. This is often not the case, and it has been observed in numerous occasions that even very tightly bound, “structural” waters may retain a significant amount of residual mobility (Denisov et al., 1997, Fischer and Verma, 1999, Matthews and Liu, 2009, Smith et al., 2004).
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“Structural” water molecules affect their surrounding not only via direct interactions (such as hydrogen-bonding network), but also by influencing the dynamical behaviour of their environment. Numerous cases have been reported when binding of the structural water affected protein flexibility (Fischer and Verma, 1999, Smith et al., 2004). The direction of such influence cannot be predicted by simple rules, as it is heavily dependent on the details of the binding site – some protein become more dynamic upon water binding (Fischer and Verma, 1999), while other ones become more rigid (Mao et al., 2000). Yet ignoring those water effects is likely to lead to substantial errors in the free energy predictions. The importance of the contributions of “structural” water molecules to binding events and its implications for drug design have been emphasised in a study by Michel et al (Michel et al., 2009).
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The traditional, enthalpy-dominated view of ligand-protein association largely neglects solvation effects, which strongly affect the thermodynamic profile of a binding event. Recently it became clear that studying the hydration state of a protein binding pocket in the apo (unbound) state should be a routine procedure in rational drug design, as the role of solvation in tuning binding affinity is critical. Solvation costs are a plausible reason why some ligands, despite fitting into a binding site, fail during experimental tests as inhibitors. Young and coworkers showed that an optimised inhibitor of factor Xa turns virtually inactive when the isopropyl group interacting in the S4 pocket of factor Xa is substituted by hydrogen: The compound (PDB code 2J4I) is characterised by Ki of 1 nM. Replacing the isopropyl group by hydrogen reduces its affinity to 39 μM. Substitution of this group by hydrogen, apart from reducing the number of favourable hydrophobic interactions, leads to unfavourable solvation of the binding pocket (Young et al., 2007, and references therein).
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Desolvation of the ligand itself may sometimes control the binding free energy. For highly hydrophilic ligands, the desolvation costs may be very high and make unfavourable contributions to the binding (Daranas et al. 2004, MacRaild et al., 2007, Syme et al., 2010). The calorimetric study of β-galactose derivatives binding to arabinose binding protein (ABP) showed dramatic differences in binding free energy between several deoxy derivatives (Daranas et al., 2004). The most likely reason of 4-deoxygalactose failing to bind to ABP is the unfavourable desolvation cost (Bronowska and Homans, unpublished data).
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Spectroscopic evidence shows that (1) water molecules in the first solvation shell (surrounding the hydrophobic solute) are more flexible that it was originally thought (Finney and Soper, 1994) and (2) hydrogen bonds at hydrophobic surfaces are weaker than it was assumed (Scatena et al., 2001). In addition, the properties of the water molecules from first two solvation shells are very different from these of bulk water, as emerged from terahertz spectroscopy results (Ebbinghaus et al., 2007, Heugen et al., 2006).
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2.4. Classical and non-classical hydrophobic effect
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The concept of the classical hydrophobic effect relies on a hydrophobic solute disrupting the structure of bulk water. This decreases entropy due to ordering of water molecules around the hydrophobic entity. Such unfavourable effects can be minimised if solute molecules aggregate. Upon aggregation, water molecules form one larger “cage” surrounding the hydrophobic aggregate, and the surface area of such aggregate is smaller than the sum of surface areas of individual (non-aggregated) solutes. This makes the entropic contribution less unfavourable and hence makes the free energy more favourable (Homans, 2007).
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If this mechanism was the sole driving force for a protein-ligand interaction, all binding events involving hydrophobic ligands would be entropy-driven. This is not the case. Several years ago, in the group of Steve Homans (University of Leeds), we studied the thermodynamics signature of ligand binding by the mouse major urinary protein (MUP). This protein is characterised by a strongly hydrophobic binding pocket and it binds a handful of very different hydrophobic ligands – long-chain alcohols and pyrazine derivatives, among others. Surprisingly, the ITC data showed that the binding was enthalpy-driven (Barratt et al., 2005). This was combined with a negative change in heat capacity upon binding - a hallmark of the hydrophobic effect.
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In order to elucidate the molecular origin of this unusual binding signature, we employed computational methods, such as molecular dynamics (MD) simulations. I will discuss the results in more details later in this chapter. The data showed that the key to this favorable enthalpy of binding of ligands to MUP seems to be the sub-optimal solvation of the binding pocket in apo (unbound) state: only a few water molecules remained there prior to ligand binding. The favourable enthalpic component was, thus, largely determined by ligand desolvation, with only a minor contribution from desolvation of the protein. Such complexation thermodynamics driven by enthalpic components have been referred to as the “non-classical hydrophobic effect”.
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2.5. Enthalpy-entropy compensation, binding cooperativity, and protein flexibility
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The enthalpic and entropic contributions are related. An increase in enthalpy by tighter binding may directly affect the entropy by the restriction of mobility of the interacting molecules (Dunitz, 1995). This phenomenon, referred to as enthalpy-entropy compensation, is widely observed, although its relevance is disputed (Ford, 2005). Such compensation, although frequently observed, is not a requirement: if it was, meaning that changes in \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t were always compensated by opposing changes in\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, optimisation of binding affinities would not be possible, which is clearly not the case.
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In connection to the enthalpy-entropy compensation, ligand-protein interactions can be cooperative, which means the binding energy associated with them is different than the sum of the individual contributions to the binding free energies. Cooperativity provides a medium to transfer information, enhance or attenuate a response to changes in local concentration and regulate the overall signalling/reaction pathway. Its effects are either positive (synergistic) or negative (interfering), depending on whether the binding of the first ligand increases or decreases the affinity for subsequent ligands. Noncooperative (additive) binding does not affect the affinity for remaining ligands and the subsequent binding sites can be regarded as independent.
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Cooperativity is often linked to pronounced conformational changes in the structure of the protein. It can be, in some cases, caused by structural tightening through the presence of additional interactions; inter-atomic distances become shorter and interaction becomes enthalpically more favorable. Evidence for such a mechanism has been reported for many ligand-protein complexes; biotin-streptavidin being one of the most extensively studied (Williams et al., 2003). In other cases, cooperativity can occur in the absence of any conformational changes of the protein, and be driven solely by changes in protein dynamics (Homans, 2005, Wand, 2001). Catabolite-activated protein (CAP) is a very good example of such dynamic allostery. CAP is a transcriptional activator that exists as a homodimer in solution, with each subunit comprising a ligand-binding domain at the N-terminal domain and a DNA-binding domain at the C-terminal domain (Harman, 2001). Two cyclic AMP (cAMP) molecules bind to CAP dimer, and this binding increases affinity of CAP for DNA (Harman, 2001). Binding of each cAMP molecule shows negative cooperativity, i.e. binding of the first cAMP molecule decreases affinity of binding of the second cAMP molecule to CAP. This is accompanied by absence of long-range structural changes. Thermodynamic analysis, performed by a combination of ITC and solution NMR, confirmed that the observed negative cooperativity was entirely driven by changes in protein entropy (Popovych et al., 2009). Thus, it is more appropriate to describe the phenomenon of cooperativity in terms of thermodynamics rather than merely conformational changes (if any such changes can be observed), since it is fundamentally thermodynamic in its nature.
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Examples above illustrate the importance of protein dynamics in binding events. Proteins tend to compensate the unfavourable entropic contribution to ligand binding by increasing their dynamics in regions distant from the ligand binding site (Evans and Bronowska, 2010, MacRaild et al., 2007) Flexible binding sites may require more flexible ligand moieties than \'stiffer\' ones. The traditional focus on the enthalpic term (direct and specific interactions) and dominance of the \'induced fit\' model has led to an overly enthalpic view of the world that neglects protein flexibility. Such view of the ligand-protein binding events, although very intuitive, is flawed by neglect of entropic contributions and – as a consequence – an impairment to correct predictions of free binding energy. Although it is true that tighter interactions make binding more favourable, the thermodynamic signature of a “good” binder does not need to be dominated by an enthalpic term.
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3. Methods
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3.1. Experimental methods
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Many experimental techniques have been developed to study various aspects of ligand-protein thermodynamics. X-ray crystallography provides very valuable information about the enthalpic contribution (hydrogen and halogen bonds, electrostatic interactions, etc). Although it focuses on static structures of ligand-receptor complexes, it also yields some information on entropic contribution. B-factors (temperature factors), obtainable for heavy (non-hydrogen) atoms of the complex under investigation, are sensitive to the mean square displacements of atoms because of thermal motions, therefore they reflect on ligand-protein dynamics. However, B-factors do not distinguish time scales of the motions and their interpretation is not straightforward. X-ray (Makowski et al., 2011) and neutron scattering (Frauenfelder and Mezei, 2010) also reflect on ligand-protein dynamics. The former one focuses on global changes in protein size and shape in a time-resolved manner, while the latter reports on motion amplitudes and time scales for positions of hydrogen atoms. Another technique useful in understanding protein dynamics both in unbound (apo) and bound (holo) forms is fluorescence spectroscopy (Weiss, 2000). Single molecule techniques also offer an opportunity to measure contributions to binding events from interacting partners individually. Hydrogen-deuterium exchange mass spectrometry (HX-MS) and related methods, have been very successful in studying protein dynamics in large supramolecular complexes (Wales and Engen, 2006). The motion of the entire complex and individual contributors, and the dynamics of the binding events can be investigated by time-resolved HX-MS (Graf et al., 2009). Another technique frequently used to study binding events is surface plasmon resonance (SPR), which allows for straightforward determination of equilibrium binding constants (Alves et al., 2005). Terahertz spectroscopy is a relatively new technique, used primarily to probe solvation of macromolecules and their complexes (Ebbinghaus et al., 2007). It is very sensitive to changes of the collective water network dynamics at the at the macromolecule-water interface. Terahertz absorption spectroscopy can also be used to probe collective modes in ligand-protein complexes (Xu et al., 2006).
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There are two groups of methods that deserve special attention in the context of thermodynamics of binding events and will be discussed more in details in the following part of this chapter. One of these is NMR spectroscopy, especially powerful for the study of ligand-protein dynamics, hence the entropic contribution to the binding free energy (Meyer and Peters, 2003). The other group contains calorimetric techniques, which are very important for the study of biological systems, their stability, and the thermodynamics of macromolecular interactions. Currently, two most popular techniques applied to investigate biological systems are differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC). The former quantifies the heat capacity and enthalpy of thermal denaturation, the latter measures the heat exchanged during macromolecular association. While DSC provides the way to estimate the stability of the system (protein, nucleic acid, ligand-protein complex, etc), ITC is an excellent tool to study the thermodynamics of binding events (Perozzo et al., 2004). Since this chapter is dedicated to the thermodynamics of macromolecular associations, in the course of this chapter I will focus mainly on ITC and its applications to study biological systems.
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3.1.1. Isothermal titration calorimetry (ITC)
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ITC measures the heat evolved during macromolecular association events. In an ITC experiment, one binding partner (ligand) is titrated into a solution containing another binding partner (protein), and the extent of binding is determined by direct measurement of heat exchange (whether heat is being generated or absorbed upon the binding). ITC is the only experimental technique where the binding constant (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tK\n\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t), Gibbs free energy of binding (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t), enthalpy (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) and entropy (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) can be determined in a single experiment (Perozzo et al., 2004). ITC experiments performed at different temperatures are used to estimate the heat capacity change (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) of the binding event (Perozzo et al., 2004).
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During last few decades, ITC has attracted interest of broader scientific community, as a powerful technique when applied in life sciences. Several practical designs emerged, but the greatest advances have happened during last 10 years. Development of sensitive, stable, and – last but not least - affordable calorimeters made calorimetry a very popular analytical procedure and ITC became the gold standard in estimations of macromolecular interactions. Given the ability of ITC to obtain a full thermodynamic description of the system studied, the technique has found widespread applicability in the study of biological systems. Apart from its versatility and simple experimental setup, ITC also has advantages over some other techniques: the experiments can be performed in a physiologically relevant buffer, no surface effects have to be taken into account, and the interacting species do not require immobilisation or labelling.
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ITC is also used for determination of binding affinity-independent reaction stoichiometry. The reaction stoichiometry is estimated from the titration equivalence point. Provided this, ITC is increasingly used in the analysis of macromolecular complexes involving multiple binding events (e.g. protein aggregation or the formation of multi-protein complexes). Systems that involve multiple binding events that occur at two or more interacting sites often demonstrate cooperativity, which is an important mechanism of regulation in biological systems (Brown, 2009).
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Using ITC it is also possible to study protonation effects, in cases when protein-ligand binding is coupled to changes in the protonation state of the system. If the formation of the complex changes the protonation state of ligand as well as that of the protein (whether free or bound), proton transfer with the solvent occurs. As a result, the signal measured by ITC will contain the heat effect of protonation/ deprotonation, contributing to the overall heat of binding. Repeating the experiment at the same pH in buffers with different ionisation enthalpies but otherwise under the same conditions allows for the determination of the number of protons released/ accepted by buffer solution. From this, the intrinsic binding enthalpy corrected by protonation heats, can be established (4).
ITC can also provide information about solvation effects. If \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t is determined at a range of temperatures, the change in the constant pressure heat capacity (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) for an interaction is given by the slope of the linear regression analysis of \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\to\n\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\ts\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t plotted vs. temperature. There is a strong correlation between \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t and the amount of desolvated (buried) surface area of a macromolecular complex. Thus, for the ligand-protein binding events, \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tis most often negative, when the complex is regarded as a reference state. Through this correlation, changes in \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t are measure of solvation state of the macromolecule and involvement of solvent effects in binding event (Perozzo et al., 2004).
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3.1.1.1. Experimental setup
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In a typical ITC experiment, a solution of ligand is injected (titrated) into a solution of the protein, in small volumes, over the time. During that time, the changes in heat resulting from the interaction are monitored (Figure 2, upper panel). Each peak represents a heat change associated with the injection of a ligand sample into the protein solution inside the ITC reaction cell. Concentrations of both ligand and protein in their respective solutions are known. As the ligand-protein system reaches saturation, the heat changes diminish until only heats of dilution are observed. A binding curve is then obtained from a plot of the heats from each titration against the ratio of ligand and protein inside the ITC cell (Figure 2, lower panel). The binding curve is analysed with the appropriate binding model to determine the thermodynamic parameters.
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ITC is a straightforward technique to accurately measure binding events with affinity range from mM up to high-nM. Problems occur when the ligand binds very tightly, in a single-digit nM and below. This is due to the titration curve becoming too steep to fit accurately. In such cases, the displacement experiments are commonly used. Such experimental setup consists of binding a low-affinity binding ligand first and then displacing it during titration with a stronger binder of interest. However, this method requires precise knowledge of binding constants of those weak binders. The experimental setup of the displacement assay is often challenging, as there are several factors increasing the error of measurements.
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Figure 2.
An example of ITC data. Raw data, representing observed changes in heat resulting from interactions are shown in the upper panel. The resulting binding curve is displayed in the lower panel (from MicroCal materials http://www.microcal.com/technology/itc.asp).
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It is worth remembering that ITC experiment not only measures the heat absorbed or released during binding reactions, but it also detects the total heat effect in the calorimetric cell upon titration of ligand. Thus, the experimental data contain contributions arising from non-specific effects, such as dilution of ligand and protein, mixing two solutions of slightly different compositions, temperature differences between the ITC cell and the titrating syringe, and so forth. In order to determine these contributions the control experiments need to be performed in order to extract the heat of ligand-protein complex formation.
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3.1.1.2. Thermodynamic content of ITC data
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The \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t determines the stability of any ligand-protein complex of interest, which makes it very useful for studies and predictions of structure–activity relationships. The conventional analysis of ITC data involves fitting an appropriate model (i.e. single- or two-site binding model) to the data, and obtaining the binding constant. Quite often, though, more sophisticated models (such as multiple interacting-site models) must be applied, if the behaviour of the system is more complex.
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As mentioned earlier, observed overall \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t can be very similar regardless of the driving force, which can be very different from one case to another. \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tcan be the same for an interaction with positive \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t and \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t (entropy-driven, binding signature dominated by the classical hydrophobic effect), an interaction with negative \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t and \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t (enthalpy-driven binding signature), or all sort of combinations of negative \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t and positive\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t. As described in the previous section, ligand-protein complexes tend to compensate for enthalpic and entropic contributions, making changes in \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t less sensitive to the molecular details of the interactions. Therefore, dissection of \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t into enthalpic and entropic contributions is of a fundamental importance for understanding of the binding energetics.
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3.1.1.2.1. Enthalpic contributions
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The change in the enthalpy represents the changes in energy associated with specific, non-covalent interaction. However, such an interpretation is too simplistic to describe experimental \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t values, and the physical meaning of observed \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t seems to be more complex. The measured changes in enthalpy are the result of the formation and breaking of many individual bonds; it reflects the loss of protein–solvent hydrogen bonds and van der Waals interactions, the loss of ligand-solvent interactions, the formation of ligand-protein bonds, salt bridges and van der Waals contacts, the re-organisation of the intra-molecular hydrogen-bonding network of the protein, solvent reorganisation near the protein surface, conformational changes at the binding site due to the binding event, and many more. These individual components may produce either favourable or unfavourable contributions, depending on the system.
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The treatment of each component individually is very challenging since the global heat effect of a particular interaction is a balance between the enthalpy of the ligand binding to the protein and to the solvent. Several approaches have been employed to investigate the energetics of individual bonds, including alanine scanning mutagenesis (Perozzo et al., 2004 and references therein), and removal of particular hydrogen bonds at the binding site (Connelly et al., 1994). However, these approaches suffer from the major bottleneck, resulting from the fact that a direct relation between the change in enthalpy and the removal of the corresponding specific interactions cannot be made a priori.
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A large part of the observed \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t is due to a bulk hydration effect, as emerged from ITC studies carried out in water and deuterium (Connelly et al., 1993). Frequently, water molecules are located at complex interfaces, improving the complementarity of the surfaces and extending hydrogen-bonding networks. This should contribute favourably to the enthalpy, but it may be offset by an unfavourable entropic contribution (Perozzo et al., 2004). The role of interfacial water was studied by lowering water activity by adding osmolytes such as glycerol to the solution. It was found that complexes with a low degree of surface complementarity and no change in hydration are tolerant to osmotic pressure (Perozzo et al., 2004, and references therein).
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3.1.1.2.2. Entropic contributions
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\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tmay be calculated directly from \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t and\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t, according to the Gibbs\' equation. Its physical representation is not straightforward. It is often related to the dynamics and flexibility of the system (Diehl et al., 2010, Homans, 2007), sometimes dubbed as a \'measure of the system\'s disorder\' (which is incorrect). It has been proposed that the \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t associated with ligand-protein binding can, at a given temperature, be expressed as the sum of several contributing effects. The main one is related to solvent effects. The burial of water-accessible surface area upon binding event should result in release of confined or interfacial water molecules to the bulk. This should contribute favourably to the total entropy of interaction. A positive entropy change is usually a strong indication that water molecules have been released from the complex surface (Jelesarov and Bosshard, 1999). On the other hand, interfacial water remaining upon binding can also contribute positively to the total entropy of the interaction (Fischer and Verma, 1999).
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Another important entropic contribution is related to the reduction of conformational (rotational and vibrational) degrees of freedom of protein side-chains. In addition to these, the ligand loses translational degrees of freedom upon binding. All these contribute unfavourably to the overall entropy of interaction. However, in some cases the protein increases the number of conformational degrees of freedom upon ligand binding, as observed by NMR and deduced from MD simulations (MacRaild et al., 2007,\n\t\t\t\t\t\t\t\tStoeckmann et al., 2008). This is likely to happen in order to partly offset the unfavourable entropic contribution from ligand binding and thus to reduce the overall thermodynamic cost of that process.
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3.1.1.2.3. Enthalpy-entropy compensation
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As mentioned in the previous section of this chapter, this phenomenon is described by the linear relationship between the change in enthalpy and the change in entropy. This means that favourable changes in binding enthalpy are compensated by opposite changes in binding entropy and vice versa, resulting in very small changes in overall free binding energy. Enthalpy–entropy compensation is an illustration of the \'motion opposes binding\' rule, and it is believed to be a consequence of altering the weak inter-molecular interactions as well as being related to solvent effects. Since both \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t and \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t are connected to\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t, the correlation between enthalpy/entropy and heat capacity changes is clear.
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Enthalpy-entropy compensation is a difficult problem to address in the context of rational molecular design. In such framework, the goal is to maximise the binding affinity of a complex of the designed compound and the protein target. The optimisation strategy requires simultaneous minimisation of both enthalpic and entropic penalties. However, reducing one of them usually means increasing the other.
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3.1.2. Nuclear Magnetic Resonance (NMR) spectroscopy
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Thermodynamics of biologically-relevant macromolecules and their complexes can be characterised by measurements using NMR spectroscopy. The basis of NMR spectroscopy is the non-zero nuclear magnetic moment of many elements, such as 1H, 13C, 15N, or 19F. When put into an external static magnetic field (B), the different nuclear spin states of these elements become quantised with energies proportional to their projections onto vector B. The energy differences are also proportional to the field strength and dependent on the chemical environment of the element, which makes NMR an ideal technique to study 3D structural and dynamical properties of the systems.
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A variety of NMR methods have been introduced to study ligand-protein interactions. These methods include one-, two- and three-dimensional NMR experiments. Many studies, to date, proved the power of stable-isotope labelling and isotope-edited NMR in the investigation of ligand-protein interactions. Recent development of techniques allowed for the study of ligand-induced conformational changes, investigating positions and dynamic behaviour of bound water molecules, and for quantification of conformational entropy. The steady-state heteronuclear Overhausser effects (NOEs) are very useful for structural analysis of three-dimensional structures of macromolecules in solution (Boehr et al., 2006, Meyer and Peters, 2003). It is important to note that the NOE occurs through space, not through chemical bonds, which makes it applicable to characterise non-covalent binding events. When ligand binds the NOEs change dramatically, and transferred NOEs (trNOEs), relying on different tumbling times of free and bound interactors, can be observed.
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Another NMR technique commonly used for identification of the ligand binding is chemical shift mapping (Meyer and Peters, 2003). Briefly, chemical shifts describe the dependence of nuclear magnetic energy levels on the electronic environment in the given macromolecule. Electron density, electronegativity, and aromaticity are among the factors affecting chemical shifts. Not surprisingly, binding event changes the chemical shifts of both interacting partners, particularly in the area of the association (e.g. protein binding pocket, protein-peptide interaction interface). Thus, changes in chemical shifts can be used to identify binding events and to describe the location of the binding.
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Ligand-protein thermodynamics can be investigated using NMR relaxation analysis, which provides an insight into protein dynamics in the presence and the absence of ligand. These results can be integrated with thermodynamic data obtained from isothermal titration calorimetry (ITC) experiments and computational results (e.g. MD simulations). For proteins, the relaxation rates of backbone (15N) and side chains (2H and 13C), can be obtained. The time scales available to NMR ranges over 17 orders of magnitude, reflecting protein motions on timescales from picoseconds to milliseconds (Boehr et al., 2006). This covers all the relevant motions of proteins and their complexes.
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Backbone and side chain (methyl groups) NMR relaxation measurements revealed the role of protein dynamics in ligand binding and protein stability (Boehr et al., 2006). Development of molecular biology techniques for incorporation of stable, 13C and 15N isotopes into expressed proteins allowed for design and application of modern multidimensional heteronuclear NMR techniques. As a consequence, the maximum size of the macromolecule studied using these techniques rose from about 10 kDa (when 1H homonuclear NMR is used) to 50 kDa and beyond (using 13C and 15N heteronuclear NMR with fractional 2H enrichment). Application of modern TROSY (transverse relaxation optimized spectroscopy) techniques further expanded the size limitations of NMR, reaching up to the 900 kDa (Fernandez and Wider, 2003).
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While NMR methodologies are being developed to study ligand-protein complexes in solid state, special techniques have been developed specifically to study protein stability and folding (Baldus, 2006), or in-cell NMR (Burz et al., 2006), providing complementary information to fluorescence studies in biological settings. In this chapter I will briefly discuss only application of relaxation analysis in solution for the study of ligand-protein thermodynamics, specifically intrinsic entropic contributions.
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3.1.2.1. Slow and fast dynamics: from dynamics to entropy
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Conformational changes that may be associated with ligand binding events generally occur on \'slow\' (microsecond to millisecond) time scales and thus report on slower motions than protein backbone and side chain fluctuations (pico-to-nanoseconds). There is no straightforward relationship between \'slow\' and \'fast\' motions. Experiments on several ligand-enzyme systems have shown that binding events, which decrease the \'fast\' motions, may increase, decrease, or not affect the \'slow\' motions (Boehr et al., 2006). This obviously has an effect on the overall entropy contribution, but this has not been fully explored.
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NMR relaxation techniques have been used to study multiple time scale dynamics of ligand-protein complexes. Their results show that even though large conformational changes occur on \'slow\' time scale, \'fast\' (pico-to-nanosecond) protein motion plays important roles in all aspects of binding event. These are typically probed by measuring three relaxation rates: the longitudinal relaxation rate (R1), the transverse relaxation rate (R2), and the NOE. These relaxation rates are directly related to the spectral density function,\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tJ\n\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\tω\n\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t. This function is proportional to the amplitude of the fluctuating magnetic field at the frequency\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tω\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t. Such fluctuating magnetic fields are caused by molecular motion in an external magnetic field, which is closely coupled to nuclear spin relaxation (Boehr et al., 2006, and references therein).
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In early studies of ps-ns time scale protein dynamics, various models for protein internal motion were used to generate different spectral density functions that were then compared to the experimental data. Subsequently, Lipari and Szabo (Lipari and Szabo, 1996) generated a spectral density function (5) that is independent of any specific physical model of motion, which is shown in equation 5 and is referred to as model free formalism.
For isotropic tumbling (ligand-protein complex tumbles in the water solution), where \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tτ\n\t\t\t\t\t\t\t\t\t\t\tm\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t is the correlation time for the overall rotational diffusion of the macromolecule, \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tis the order parameter, and\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\tτ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tτ\n\t\t\t\t\t\t\t\t\t\t\t\t\tm\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t+\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tτ\n\t\t\t\t\t\t\t\t\t\t\t\t\te\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t,where \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tτ\n\t\t\t\t\t\t\t\t\t\t\te\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t is the time scale (ns) for the bond vector internal motions. An order parameter of 1 indicates complete restriction of internal motion, and \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t indicates unrestricted isotropic internal motion. It should be emphasised that \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t parameters have a straightforward physical interpretation. The simplest model relates \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t to \'diffusion in a cone\' with semi-angle\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tθ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t, and is shown in Figure 3.
Physical interpretation of \n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t order parameters. \n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tcan be interpreted as a measure of a free rotation of a bond vector (here – N-H) in a cone. The semi-angle \n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tθ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t is displayed. Smaller \n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t parameters correspond to more flexible bond vectors. \n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tmeans unrestricted rotation of the bond vector.
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There were attempts to relate order parameters to structural characteristics of proteins and ligand-protein complexes. It was observed that amino acids with smaller side chains tend to show – intuitively - greater backbone flexibility than those with bulkier side chains (Goodman et al., 2000). However, the variation of backbone amide \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t parameters is larger than the differences between the averages for different amino acid types. Backbone amide order parameters are also only weakly affected by secondary structure elements, with loops having only slightly smaller average \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t N-H values than helices or beta-turns (Kay et al., 1989). Backbone \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t N-H values can be predicted from structures using a simple model that takes account of local contacts to the N-H and C=O atoms of each peptide group (Zhang and Brüschweiler, 2002).
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A more sophisticated model for predicting dynamics from structure has recently been reported (tCONCOORD) (Seeliger et al., 2007). tCONCOORD allows for a fast and efficient sampling of protein\'s conformational degrees of freedom based on geometrical restraints. Weak correlation between side chain order parameters and contact distance between the methyl carbon and neighboring atoms, with solvent exposure (Ming and Brüschweiler, 2004), and amino acid sequence conservation patterns (Mittermaier et al., 2003) have been reported in literature. These results demonstrate that protein dynamics are strongly affected by the unique architecture of the protein as well as the environment. Thus, it cannot be readily predicted by the bioinformatic techniques, based on the primary/secondary sequence analysis. Developing a fast and reliable method of assessment of protein dynamics is, nevertheless, crucial for predictions of ligand-protein interactions - as it will be shown in the course of this chapter, dynamics affects all stages of molecular recognition events.
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Order parameters can be related to entropy through the relationship developed by Yang and Kay (1996). This formalism quantifies the conformational entropy associated with observable protein motions by means of a specific motion model. For a wide range of motion models, the functional dependence of entropy on the \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t parameter was demonstrated to be similar (Yang and Kay, 1996). This suggests that changes in \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t can be related to the conformational entropy change in a model-independent manner. This approach has many advantages: it is straightforward, relatively free of assumptions (the requirement is that the internal motions are uncorrelated with the global tumbling of the macromolecule), and applicable to both NMR experiments and theoretical approaches (MD simulations). Moreover, since \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t parameters are measured per bond vector, this approach enables site-specific reporting of any loses, gains, and redistributions of conformational entropy through different dynamic states of the ligand-protein complex.
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However, the model-free formalism can give only a qualitative view of micro-to-millisecond time scale motions. Failure to correctly account for anisotropic molecular tumbling and the assumption that all motions are un-correlated seriously compromises the usefulness of this approach for studying dynamics associated with large conformational changes or concerted motions. Because of the time scales, alternative approaches must be implemented to study motions occurring at a millisecond time scale (e.g. R2 relaxation dispersion).
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3.1.3. Combination of ITC and NMR
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As described, ITC obtains free energy as the global parameter, thus, effects like ligand-induced conformational changes, domain-swapping, or protein oligomerisation, which contribute to the overall\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t, will not be resolved. In order to assess the role those factors play in a binding event, a combination of ITC and other techniques (such as NMR) need to be used. A combination of ITC and NMR proves useful in studying cooperativity phenomena. Heteronuclear NMR spectroscopy is one of the few experimental techniques capable of measuring the occupancies of individual binding sites on proteins and therefore determining microscopic binding affinities. Coupling this site-specific data (e.g. chemical shift mapping and/or relaxation analysis data) with the macroscopic binding data from ITC allows a complete description of the binding properties of the system. A method of determining cooperativity using heteronuclear solution NMR spectroscopy has been described using an isotope-enriched two-dimensional heteronuclear single-quantum coherence experiment (2D HSQC) (Tochtrop et al., 2002). The ligands are isotopically labelled (usually 1H, 15N, or 13C), while the receptor remains unlabelled. Spectra are acquired at different molar ratios and the peak volumes are integrated. Isotherms are generated by plotting the peak volume integration against molar ratio. The data is then fitted to site-specific binding models to obtain the thermodynamic parameters (Brown, 2009).
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3.2. Computational approaches
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Computational approaches to ligand-protein interaction studies have great potential and the development of various methods, briefly described in this chapter, have been truly outstanding. However, every method – computational, experimental alike - has its limitations and computational methods should not be used in a \'black box\' manner; one should beware of the \'Garbage In Garbage Out\' phenomenon. Yet it is evident that theoretical approaches have finally come to the stage that makes rational molecular design truly rational.
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During a binding event, the ligand may bind in multiple orientations. The conformation of either of the interacting partners can change significantly upon association. The network of intramolecular interactions (e.g. hydrogen bonds, salt bridges) can dramatically change (breaking and/or creating new contacts), and new intermolecular interactions occur. Water molecules and ions can be expelled upon binding, or – on the contrary – bind more tightly. Finally, conformational or solvation entropic contributions may play significant role, affecting the free energy in a way which is difficult to predict.
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Growing amount of calorimetric data available allows the investigation of the thermodynamic profiles for many ligand-protein complexes in detail. When structural data (crystal, NMR) are available as well – and often it is the case - it is very appealing to speculate about the link between the structure of the complex and the thermodynamics of the binding event. However, such speculations are challenging. It is important to bear in mind that both enthalpic and entropic contributions to the free energy terms obtained from ITC experiments are global parameters, containing a mixture of different contributions, which can have either equal or opposing signs and different magnitudes. This may lead to various thermodynamic signatures of a binding event. Moreover, \'structural\' interpretation of intrinsic entropic contributions is notoriously difficult. Hence, the experimental thermodynamic data cannot be easily interpreted on the basis of structural information alone. Last but not least, the contribution from the solvation effects is difficult to get insight into, and although direct experimental estimations of solvation free energy have been attempted, these always require additional assumptions (Homans, 2007, Shimokhina et al., 2006).
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No doubt, a great advantage of theoretical approaches lies with gaining an insight about each of those contributions and their de-convolution. Binding events (ligand-protein binding pose and the strength of their interactions) can be predicted by molecular docking, albeit intrinsic entropic contributions and solvation effects are usually ignored. Dynamic behaviour of proteins and ligands can be studied using extensive molecular dynamics (MD) simulation, which, combined with experimental NMR and ITC data, provide extremely valuable information on configurational entropy changes upon binding event, and hence about the intrinsic entropic contribution to the free energy. The global free energy changes can be studied by free energy perturbation (FEP) calculations, or related methods, such as thermodynamic integration (TI). Molecular docking methods allow for a quick assessment of enthalpic contributions, while solvent effects can be studied either by quantum chemical (QM) calculations (e.g. COSMO model), hybrid QM/MM schemes, or FEP-related methods.
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Theoretical approaches allow also for investigations of transient phenomena, e.g. short-living alternative conformers from an ensemble that contribute to the binding event but which cannot be readily observed. In a situation – which is not uncommon – when an experimental structure of the protein target or a part of it is missing (such as in cases of most G-protein-coupled receptors), computational approaches allow the generation of such structures (e.g. by homology modelling, threading, or ab initio predictions) and its use for predictions which can be validated experimentally, despite of the absence of protein structural data. Therefore, usage of theoretical methods is indispensable – not only for the interpretation of the existing experimental data, but also to direct and design new experiments.
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Because of space limitations, only two theoretical methods, which are the most relevant for thermodynamics of molecular binding events, will be briefly discussed: MD-related methods (which includes MD simulations, FEP-like approaches, methods which use MD algorithms with enhanced sampling, and hybrid QM/MM schemes), and quantum chemical (QM) calculations. This division is not strict and many of these methods overlap, e.g. QM/MM methods use both MD simulations and QM calculations, and FEP-like methods have many flavours, including hybrid QM/MM-FEP.
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3.2.1. Molecular Dynamics (MD) simulations
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Molecular dynamics (MD) simulation consists of the numerical solution of the Newton\'s equations of motion of a system (e.g. protein, or a ligand-protein complex in water environment). The potential energy of the particle system is described by a function called force field (6). The potential energy of the system (U) is described as a sum of energy terms for covalent bonds, angles, dihedral angles, a van der Waals non-bonded term, and a non-bonded electrostatic term (Cornell et al., 1995). Since the kinetic energy is also taken into account, the system is able to move across the energy barriers on the potential energy surface, which implies substantial changes (e.g. conformational) during the simulation.
The principles of MD simulations, algorithms used, and different types of force fields applied (all-atom, united atom, coarse-grain, etc) have been described in many publications (Klepeis et al., 2009 and references therein). MD methods rely on quality of the force field (parameters, inclusion of non-additive effects, etc), description of solvent effects, adequate sampling, and quality of initial structures used for the simulations. The quality of the results relies also on the duration of the simulation. There are limits on the time scales at which the system of interest can be considered. Simulation runs are fairly short: typically nanoseconds to microseconds, rarely extending to miliseconds, if super-fast computers are employed. Since biological processes (ligand-protein binding, large conformational changes, etc) typically occur ar micro-to-milisecond scales, one needs to assess whether or not a simulation has reached equilibrium before the averages calculated can be trusted. Furthermore, the averages obtained need to be subjected to a statistical analysis, to make an estimate of the errors.
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MD methods have been widely employed to study ligand-protein binding phenomena, conformational changes, solvent effects, and to assess individual contributions to the binding free energy. These methods are particularly useful in assessing the conformational entropic contribution to the free energy. Information about ps-to-ns time scale molecular motions can be readily obtained from the MD simulation trajectory and analysed either through diagonalisation of the covariance matrix of displacements of atomic Cartesian coordinates - quasi-harmonic analysis, Schlitter\'s approach (7,8), analysed through principal component analysis (PCA), or quantified NMR-like via generalised order parameters. Entropy changes can be estimated from the MD trajectory through Yang and Kay\'s relationship (9). The order parameter analysis has the advantage of being able to calculate order parameters by-vector, thus providing site-specific information on flexibility and hence intrinsic entropic contribution. Computed parameters can be also directly compared to the experimental results of NMR relaxation analysis (Best and Vendruscolo, 2004). In last few years several studies proved the success of this methodology in estimating of entropic contributions to the binding thermodynamics.
Free energy perturbation (FEP) is used to calculate free energy differences between two states from MD simulations. These two states can represent, for instance, unbound (apo) protein and a ligand-protein complex (holo), or two ligand-protein complexes with different ligands. In the framework of FEP, the difference in the free energy difference for two states is obtained from the Zwanzig equation (10).
where A and B represent two states (e.g. apo and holo protein), G is the difference between free energies of both states, \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tk\n\t\t\t\t\t\t\t\t\t\tB\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tis the Boltzmann\'s constant, T is the temperature, and the triangular brackets denote an average over a simulation run for state A.
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FEP calculations converge properly only when the difference between these two states is small enough; therefore it is usually necessary to divide a perturbation into a series of smaller \'steps\', which are calculated independently.
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Thermodynamic integration (TI) is a related method to calculate free energy differences. Since the free energy can be expressed by its relation to the canonical partition function, the free energy difference in two different states can be used to calculate the difference of potential energy. TI calculations are usually executed by designing a thermodynamic cycle (Figure 1), and integrating along the relevant path. The path can be either a real chemical process or an artificial change (e.g. substitution of a methyl group by hydrogen atom).
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The MD methods, despite their numerous successes, suffer of two major bottlenecks. One is the results are critically dependent on the force field used, therefore requires caution when use of appropriate force field and parameters. Many modern force fields are parametrised on experimental NMR data, some are able to include – to some extent – non-additive effects (electronic polarisation). Application of QM/MM schemes allow the inclusion of quantum effects to some extent. Another bottleneck is related to the adequacy of sampling. It is known that – due to relatively short time scales investigated – only some subsets of potential conformational changes can be observed, and often the system gets \'stuck\' in a minimum, which does not have to be the global one. This makes the results heavily biased towards the starting structure and is very likely to underestimate the degree of molecular motions observed in the system. Prolonging the simulation time helps to solve the sampling problem only to some extent, and significantly increases the computational cost of MD simulations. Thus, in order to overcome the sampling issue, various enhanced sampling techniques have been employed. One of such methods, frequently used, is replica exchange MD (REMD), which attempts to overcome the problem of multiple-minima by exchanging temperatures of several replicas of the system. These replicas are non-interacting with each other and they run at different temperatures. REMD is also called “parallel tempering” (Earl and Deem, 2005). Another approach used to improve sampling is to construct the bias potential and add it to the potential energy function of the system (force field). This group of methods, referred to as umbrella sampling methods, consist of metadynamics (Laio and Gervasio, 2008), conformational flooding, and accelerated dynamics (Lange et al., 2006). The core feature of metadynamics is the construction of so-called reference potential, which is one that is the most similar to the actual potential. That is, repulsive markers are placed in a coarse time line in a space that is spanned by a small number of relevant collective variables. These markers are then placed on top of the underlying free energy landscape in order to push the system to rapidly accumulate in the initial basin by discouraging it from revisiting points in configurational space. In this way, the system is allowed to escape the lowest transition state as soon as the growing biasing potential and the underlying free energy well exactly counterbalance each other, effectively allowing the simulation to escape free energy minima.
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3.2.2. Quantum mechanical (QM) calculations
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Ligand-protein interactions can be driven by quantum effects. These include charge transfer, halogen bonds, or polarisation. Stabilisation energy related to charge transfer can be several kcal/mol and force field-based schemes cannot describe this stabilisation correctly.
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‘Conventional’ QM calculations, using HF, DFT, or semi-empirical methods provide a way to obtain the ground state energy of a ligand-protein system or a part of it. Most programs based on these are capable of studying molecular properties such as atomic charges, multipole moments, vibrational frequencies, and spectroscopic constants. In addition, there are methods allowing the study of excited-state processes, such as time-dependent DFT or restricted open-shell Kohn-Sham (ROKS) (Li and Liu, 2010).
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The application area of QM methods is vast. QM calculations are used for charge derivation for molecular dynamics simulations, for description of direct interactions (hydrogen bonds, halogen bonds, aromatic stacking), for calculations of pKa, protonation, redox states, and for studying solvation effects, such as computing free solvation energies.
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Derivation of accurate charges for a system being studied is an important step in preparation for MD simulation. Failure in charge representation will inevitably lead to incorrect results. Derivation of charges is done using QM calculations, usually in several steps, involving optimisation, electrostatic potential generation, and fitting charges into atoms. RESP methodology, based on charges derived from ab initio HF/6-31G* level of theory has been for many years a standard in deriving charges for MD simulations (Bayly et al., 1993, Cieplak et al., 1995, Cornell et al., 1993).
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Charge distribution is also required for the calculation of the solvation properties using conductor-like screening model (COSMO) (Klamt and Schüürmann, 1993). COSMO, just like any other continuum solvent approach, approximates the solvent by a dielectric continuum, surrounding the solute molecules outside of a molecular cavity. In COSMO, the polarisation charges of the continuum, caused by the polarity of the solute molecule, is derived from a scaled-conductor approximation (hence the name). In this way, the charge distribution of the molecule, which can be obtained from the QM calculations, and the energy of the interaction between the solvent and the solute molecule can be determined.
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QM calculations are also used for force field development, such as adding new parameters and incorporating non-additive effects. Several studies indicate that non-additive effects (.e.g. electronic polarisation) significantly affect binding affinities of many ligands (Ji and Zhang, 2008, 2009 and references therein). Electrostatic interactions are critically dependent on charge distribution around both interacting species, and this distribution is heavily dependent on the conformation (geometry) of the complex. Description of hydrogen bonding is also affected by electronic polarisation – some hydrogen bonds, which are found broken during MD simulation using \'conventional\' force fields are found to be stable, when non-additive force field is used (Ji and Zhang, 2009). Corrections for polarisation can be added to MD force fields in order to derive protein charges more accurately and provide a better description of electrostatic interactions. Protein polarisation is important for stabilisation of the native structures of proteins. MD simulations indicate that inclusion of polarisation effects not only improves the description of protein native structures, but also distinguishes native from decoy dynamically: the former are more stable than the latter under the polarised force fields. These observations provide strong evidence that inclusion of polarisation effects in calculations of ligand-protein interactions is likely to greatly improve accuracy of such calculations.
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QM methods are also used in molecular docking. The application of QM methods to molecular docking was pioneered by Raha and Merz (2004), who developed a semi-empirical QM-based scoring function and studied ion-mediated ligand binding processes. Their conclusion was that quantum chemical description is required for metal-containing systems, mainly because of poorly-defined atom types of metal atoms in most of the force field parameters, which cannot describe the interactions between a small molecule ligand and a metal ion in the active site of the protein.
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Applicability of QM methods to study ligand-protein system has been discussed in literature (Raha et al., 2007, Stewart, 2007). As well as these successes, many examples of the failure of QM approaches have been demonstrated. However, it should be kept in mind that most of these studies were based on either DFT, or semi-empirical Hamiltonians, which do not describe van der Waals interactions and hydrogen bonding terms of ligand-protein interactions correctly. This is, indeed, a serious limitation of “fast”, hence more popular QM methods. A straightforward way to solve this problem is to add additional correction terms to the QM energy. It has been demonstrated that the addition of the dispersion energy and corrections for hydrogen bonds improved the performance of semi-empirical QM methods dramatically (Rezac et al., 2009). The recently developed PM6-DH2 method (Fanfrlik et al., 2010) yields, to date, the most accurate results for non-covalent interactions among the semi-empirical QM methods. For small non-covalent complexes, the results obtained were comparable to the high-level wave-function theory-based calculations within chemical accuracy (1 kcal/mol) (Rezac et al., 2009).
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Another major bottleneck of QM methods applied to studying ligand-protein interactions thermodynamics is the size of the system. DFT can handle up to 150 atoms, highly-accurate methods such as coupled-clusters can handle a few tens of atoms and require very fast computers and long computing times. This limitation of the size of the systems that can be studied seriously compromises its usage in the study of ligand-protein thermodynamics. For instance, the usage of the linear scaling algorithm MOZYME (Stewart, 2009) based on the localised orbitals allows size increases to systems as large as 18 000 atoms and above, which allows calculation of very large ligand-protein complexes. Due to these developments QM methods have become, therefore, very useful for fast and highly accurate predictions of ligand-protein interactions energetics.
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An alternative approach is to use so-called divide-and-conquer (DIVCON) algorithm (Dixon and Merz, 1996, 1997). The principle is to divide a large system into many smaller subsystems, separately determine the electron density of each of these subsystems, and then to add the corresponding contributions from each subsystem in order to obtain the total electron density and the energy.
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4. Examples
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In the previous sections of this chapter, I briefly introduced the forces governing macromolecular associations and characterised methods commonly used to assess these contributions. Here, I will illustrate on several examples of \'real\' ligand-protein systems and the way how their binding thermodynamics is studied.
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4.1. Hydrophobic versus hydrophilic binding pocket: MUP and HBP
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Both histamine-binding protein (HBP) and mouse major urinary protein (MUP) are members of lipocalin family of proteins, so their overall structures are similar (Figure 4). The binding pocket of HBP contains a number of polar and charged residues, hence it is an example of a \'hydrophilic\' binder. In contrast to HBP, the binding pocket of MUP is very \'hydrophobic\'. Surprisingly, both HBP and MUP are characterised by similar overall entropy of ligand binding.
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In our recent study (Syme et al., 2010) we compared the driving forces for binding between these two proteins in terms of entropic contributions from ligand, protein, and solvent. We performed an extensive study combining x-ray crystallography, NMR spectroscopy, ITC, MD simulations, and QM calculations.
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4.1.1. Structures of HBP and MUP
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The structure of MUP was solved both by x-ray crystallography and solution NMR (Barratt et al., 2005, Kuser et al., 2001, Timm et al., 2001). Several ligand-MUP complexes were studied, including long-chain alcohols, pyrazine derivatives, and pheromones as ligands. Regardless of the chemical nature of ligand, the protein structures are very similar to each other: the desolvated ligand, which occupies the central, hydrophobic binding pocket, causes very few conformational changes (Figure 4, left panel).
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The crystal structure of HBP complexed with histamine revealed two binding sites for the ligand: one of them possessing considerably higher affinity than the other (Figure 4, right panel) (Syme et al., 2010). Therefore, in order to simplify the thermodynamic analysis of ligand binding, a mutant of HBP was designed. In this mutant, denoted as HBP-D24R, negatively charged aspartic acid D24 inside the “low” affinity site was replaced by larger and positively charged arginine. This abolished binding of ligand to the “low” affinity site.
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Figure 4.
Crystal structures of MUP (left panel) and HBP (right panel). Both MUP and HBP are displayed with their ligands bound: octanediol (purple), and histamine (pink). Ligands are represented as VDW spheres. For MUP, superimposed structures of apo (blue) and holo (dark cyan) protein are showed, in order to display a lack of major conformational changes associated with ligand binding. For HBP, the second (low affinity) binding site is showed and coloured yellow.
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4.1.2. Calorimetric studies of MUP
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Given that the binding pocket of MUP is very hydrophobic, an entropy-driven binding signature might have been expected for ligand-MUP interactions. Surprisingly, global thermodynamics data obtained for pyrazine ligands (Barratt et al. 2004) and alcohols (Barratt et al., 2006) showed that binding is driven by favourable enthalpic contributions, rather than the classical hydrophobic effect. The only hydrogen bond that could be formed between a ligand and the protein binding site involved the hydroxyl group of tyrosine Y120. Barratt et al. (2004) reported that ITC measurements on the binding of isobutyl-methoxypyrazine (IBMP) to the Y120F (phenylalanine side chain lacks hydroxyl group) mutant showed slightly reduced enthalpy of binding compared to wild-type MUP, but the binding was nonetheless enthalpy-driven.
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Binding of long-chain alcohols, such as n-octanol, n-nonanol, and 1,8 octan-diol was characterised by similar thermodynamic signature. Contrary to expectations, binding was enthalpy-driven (Barratt et al., 2006). Each complex was characterised by a bridging water molecule between the hydroxyl group of Y120 and the hydroxyl group of ligand. The thermodynamic penalty to binding derived from the unfavourable desolvation of 1,8 octan-diol (+21 kJ/mol with respect to n-octanol, which came from an additional hydroxyl group facing a hydrophobic pocket) was partially offset by a favourable intrinsic contribution.
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Figure 5.
ITC data for obtained for HBP. Binding curves for HBPD24R mutant and wild-type BP are displayed in left and right panel, respectively. Bottom panel shows thermodynamic parameters for mutated and wild-type HBP, obtained from ITC measurements.
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4.1.3. Calorimetric studies of HBP
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Typical ITC isotherms for the binding of histamine to the HBP-D24R mutant (top panel) and wild-type HBP (middle panel), and the resulting thermodynamic parameters (bottom panel) are shown in Figure 5. Consistent with structural data, histamine bound to wild-type HBP with 2:1 stoichiometry (both binding sites occupied), while to mutant with 1:1 stoichiometry. In both cases, histamine bound with high affinity (Kd in nanomolar range), but the binding enthalpies and entropies were somewhat different, even though binding was largely enthalpy-driven in both cases. These differences in thermodynamic details are a manifestation of the enthalpy-entropy compensation, introduced in the previous sections of this chapter: wild-type HBP binds histamine with similar affinity than D24R mutant, but its enthalpic contribution is more favourable than that of D24R, at the expense of the entropic contribution, which is less favourable in wild-type HBP than in D24R mutant.
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A major concern regarding the data shown in Figure 5 was the possibility of proton exchange (release or binding) during the binding event. In such case, the observed enthalpy change \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\twould contain contributions from the ionisation of the buffer\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\to\n\t\t\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t, as explained previously. To assess this effect, titrations between histamine and HBP-D24R mutant were performed in two different buffers, characterised by very different values of the ionisation enthalpy (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\to\n\t\t\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t47.45 kJ/mol and 3.6 kJ/mol, respectively). Thus, any contribution from proton exchange should be easily detectable on the basis of the differences in \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tH\n\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t for the histamine-HBP interaction. Obtained enthalpies (-58 and -61 kJ/mol, respectively) showed that there were no significant protonation effects associated with histamine-HBPD24R binding.
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4.1.4. NMR relaxation measurements
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In order to gain deeper insight into the entropic contribution to binding of ligands to HBP and MUP, 15N NMR relaxation measurements were employed to probe per-residue conformational entropies for backbone amides for the free (apo) protein and for the ligand-protein complexes. Backbone 15N longitudinal and transverse relaxation rates (R1 = 1/T1 and R2 = 1/T2, respectively) were determined for the free protein and the complexes with ligands. Amide 15N and 1H-15N resonance assignments in the apo-HBP, apo-MUP, and the ligand-protein complexes were determined by use of conventional three-dimensional triple-resonance experiments.
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Figure 6.
Backbone amide entropy changes for HBP-D24R, quantified as differences between entropy of holo and apo protein for each protein residue. The plot shows histamine binding – induced changes in entropy assessed by 15N relaxation measurements.
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For both HBP and MUP, both positive and negative changes in local backbone entropy were observed. Entropy changes were not restricted to the binding pocket but were dispersed over the protein (Figure 6). For HBP as well as MUP, \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tsummed over backbone amides was close to the error value, i.e., an overall change in backbone entropy that was not statistically different from zero (Syme et al., 2010).
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4.1.5. Molecular dynamics (MD) simulations
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MD simulations were used to examine the solvation of the binding pocket of MUP and HBP. The MUP binding pocket was found virtually devoid of water, even without any ligand bound (Barratt et al., 2006). Even if water molecules were artificially “forced in” at the beginning of the MD simulation, they did not remain inside the pocket. This observation contributed to the explanation of the unexpected thermodynamic signature of the ligand binding to MUP, measured by ITC.
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An average of five to six water molecules is observed in the binding pocket of HBP over the simulation time in the absence of ligand. Analysis of the diffusion and rotational correlation functions of these solvent molecules suggested that their dynamic behaviour was very similar to those of bulk water (Syme et al., 2010), which suggests that the return of these water molecules to bulk solution on ligand binding would not offer any significant contribution to the binding entropy.
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For HBP, side chain entropies were computed from the MD simulation. Moreover, as a test of the robustness of these simulations, backbone amide entropy changes were calculated from the MD simulation and compared to NMR data. The comparison was very favourable (16.4 ± 1.0 kJ/mol versus 12.4 ± 9.8 kJ/mol), which raised confidence in the applied methodology. Contribution from the protein side chains was estimated at +17.4± 1.8 kJ/mol (Syme et al., 2010). Taken together, these data strongly indicate an overall increase in entropy on ligand binding. This observation, although counter-intuitive, it is not without precedent in the literature (MacRaild et al., 2007, Stoeckmann et al., 2008).
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MD simulations were also used to estimate the entropic contributions from the ligand. A contribution from the loss in vibrational degrees of freedom of histamine on binding to HBP was estimated using the Schlitter\'s method ( Schlitter, 1993), leading to an unfavorable contribution of \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t≈\n\t\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t\t22 \n\t\t\t\t\t\t\t\t\t±\n\t\t\t\t\t\t\t\t\t 2\n\t\t\t\t\t\t\t\t\t.4\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t kJ/mol.
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In addition to this contribution, there was an assumption that internal degrees of freedom of the ligand are heavily constrained upon binding. The unfavorable contribution from the three relevant internal degrees of freedom of histamine amounted to \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≈\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t-12 kJ/mol (Lundquist et al., 2000). In addition, the entropic contribution from the loss of translational and rotational degrees of freedom of the ligand depends on the logarithm of the molecular mass, and on the basis of earlier work this represents an unfavorable contribution that can be estimated as \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≈\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t-25 kJ/mol (Turnbull et al., 2004).
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4.1.6. Solvation thermodynamics estimation
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For MUP ligands, it was possible to measure their solvation free energies directly, using the water/vapor partitioning experiments (Shimokhina et al., 2006). For histamine, experimental measurements could not be done due to non-volatility of histamine, and hence the ligand free solvation energies were calculated quantum-chemically, using the COSMO model (Klamt and Schüürmann, 1993). Prior to running calculations, the ligand was optimised using ab initio QM calculations (RFH/6-31G* basis set). The optimised structure was subjected to COSMO calculations at three different temperature settings (270, 300, and 330 K) in order to extract the enthalpic and entropic contributions to the free energy of solvation, using the finite-difference approach. The approximation used therein was based on the assumption that the heat capacity is constant over a certain range of temperatures near the target temperature, T. In the case of solute molecules solvated in water, this approximation usually holds near room temperature for temperature ranges (denoted as \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tT) as wide as 50 K. Using the finite-difference approximation, the entropy can be approximated at the target temperature as in equation (11):
where \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t denotes entropy at the target temperature, \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tG\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tis the free energy of solvation energy, and \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t is the temperature difference. For the calculations presented here \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t was 30 K.
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Obviously, it was very difficult to assess the accuracy of such calculations, but the experimental solvation thermodynamics of two related “fragments” of histamine have been reported (Cabani et al., 1981). Comparison (Table 1) shows that the solvation free energies are reproduced very well, and the solvation entropies and enthalpies reasonably well (for n-propylamine) compared with experiment, which lends some confidence in the computed values for histamine.
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Table1.
Solvation parameters: free energy, enthalpy, and entropy at temperature 300 K for histamine and related ligands. All values are given in kJ/mol. Experimental values (exp) are taken from Cabani et al. (1981).
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4.1.7. Driving forces for ligand binding by MUP and HBP
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Ligand binding to both HBP and MUP was strongly enthalpy-driven. The overall entropy of binding was the same within error for both MUP and HBP, yet the contributions from protein, ligand, and solvent are very different.
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In the case of HBP, the dominant entropic contribution to binding arises from ligand desolvation, with a significant contribution from protein degrees of freedom. This contribution is favourable. However, the overall entropic contribution to binding is unfavourable, which indicates that the entropic contribution from desolvation of the protein binding pocket is strongly unfavourable.
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In MUP, a favourable contribution to binding entropy is also derived from ligand desolvation, but is unable to overcome the unfavourable contribution from “freezing” ligand degrees of freedom upon binding. The favourable entropic contribution from desolvation of the protein binding pocket that one would predict in a “classical” hydrophobic interaction is absent in MUP, since the occluded binding pocket is substantially desolvated prior to binding. This phenomenon has subsequently been observed in other proteins, such as streptavidin and HIV-protease receptors (Young et al., 2007).
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As suggested by MD simulations, there are around 6 water molecules (on average) occupying the binding site of HBP prior to ligand binding. As already stated, no significant change in entropic contribution should arise from displacing these waters upon ligand binding, since their dynamic behaviour inside the pocket is similar to the behaviour of the bulk water. However, four water molecules are sequestered in the binding pocket in the histamine-HBP complex. These waters are significantly more ordered than the bulk water (Syme et al., 2010), which contributes negatively to the entropic term of binding free energy. This unfavourable entropic contribution can be estimated as about \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t\t30\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t to \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t\t40\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t kJ/mol, which is qualitatively consistent with the observed sign of \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\tP\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t on binding.
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In conclusion, in the case of HBP, we found favourable entropic contributions to binding from desolvation of the ligand. However, the overall entropy of binding was unfavourable due to a dominant unfavourable contribution arising from the loss of ligand degrees of freedom, together with the sequestration of solvent water molecules into the binding pocket in the complex. This can be contrasted with MUP, where desolvation of the protein binding pocket made a minor contribution to the overall entropy of binding given that the pocket is substantially desolvated prior to binding.
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4.2. An integrated picture of galactose binding to Arabinose Binding Protein (ABP)
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The arabinose binding protein is found in the periplasm of Gram-negative bacteria. It belongs to the family of proteins, which extert their biological function as components of osmotic shock-sensitive transport systems for sugars and amino acids (Vyas et al., 1991). Besides its biological context, ABP is a very well-defined model system for structure-activity relationships in the hydrophilic ligand binding systems. As demonstrated by ITC, ABP interacts with its natural ligands, namely L-arabinose and D-galactose, and their deoxy derivatives (Daranas et al., 2004). The interactions are enthalpy-driven. The galactose-ABP interactions served as a model for interactions between hydrophilic ligands and hydrophilic binding pockets. Such choice of the model system was made mainly because of the large unfavourable entropic contribution to binding, whose origin was difficult to understand in the framework of the current ligand-protein interaction paradigms. Such a large entropy change upon ligand binding is frequently observed in proteins interacting with carbohydrate ligands, and it contributes to the fact that these interactions are notoriously challenging for predictions and design. In order to address those issues, we employed a combination of solution NMR and MD simulations.
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4.2.1. NMR measurements of ABP
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An essential prerequisite to NMR studies of protein dynamics was the establishment of the 1H, 15N and 13C resonances that contributed to the investigated spectra. These assignments have been obtained for the complex of ABP with the ligand D-galactose using conventional triple-resonance assignment strategies (Daranas et al, 2004). Assignments for the unbound (apo) ABP were determined from these results, using an approach that combined conventional triple-resonance assignment strategies and 1H-15N heteronuclear single quantum coherence (HSQC) titrations of ABP with 1-deoxy-galactose, which is a fast-exchanging ligand (MacRaild et al., 2007).
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From these assignments, a comparison was made of the chemical shifts of the backbone amide resonances of ABP in the unbound state and in the complex (Figure 7). Large chemical shift changes were observed in the binding site area and in the region linking the two domains of ABP. This suggested that ligand binding might be associated with a substantial conformational change in ABP (Figure 8). Such conformational change (domain reorientation) upon binding is observed in other members of the periplasmic-binding protein family, and have been proposed for ABP on the basis of the results of small-angle X-ray scattering and theoretical studies (Mao et al., 1982, Newcomer et al., 1981). Smaller changes in chemical shift were also observed at sites distal to the binding site, which suggests that small conformational changes, resulting from protein dynamic behaviour, occur upon the binding event.
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Figure 7.
Chemical shifts for galactose binding to ABP. Changes in backbone amide chemical shifts are plotted against protein residue number.
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Figure 8.
Conformational changes between apo-ABP (light blue) and galactose-ABP complex. The apo protein is much more \'open\' than the complex. Large conformational changes are observed in the hinge region (coloured red), and the reorientation of N-domain (yellow) and C-domain (dark blue) towards each other is quite pronounced. Bound galactose is coloured cyan and displayed as VDW spheres. Side chains of several residues involved in direct interactions with ABP are showed and coloured dark cyan.
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Full sets of 15N relaxation measurements were made for ABP in its apo form and in complex with D-galactose, L-arabinose and D-fucose. The analysis of these measurements, assessed by means of Lipari–Szabo model-free approach (1982), allowed for extraction of the information on the extent of \'fast\' (ps-ns time scale) motion of the protein backbone and 15N-containing side chains. Differences in Lipari-Szabo generalised order parameters (\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t) between apo- and holo-ABP and were interpreted in terms of changes in dynamics accompanying the binding event. Thus, observed dynamic changes could be related to binding thermodynamics by means of the relationship between changes in order parameters derived from NMR relaxation and changes in conformational entropy. Because \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t parameters are specific to individual bond vectors, the described approach offered an unprecedented degree of structural resolution in thermodynamic analysis of protein function.
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Surprisingly, generalised order parameters for apo ABP were, in general, larger than for the ABP–galactose complex. This suggests that \'fast\' (pico- to nanosecond time scale) motions are more extensive in the ligand-protein complex than in the unbound protein.
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4.2.2. Molecular dynamics (MD) study
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To confirm and further explore NMR relaxation result, we performed MD simulations of ABP in complex with galactose and its unbound state. In order to make a direct comparison between the observed order parameters and the simulations, backbone amide order parameters were calculated from the MD trajectory (MacRaild et al., 2007). A very good agreement between measured and calculated order parameters was observed, albeit with a small tendency for the simulation to underestimate the experimental values. The calculated changes in order parameter upon ligand binding reproduced the changes measured by NMR excellently, showing an approximately uniform decrease in order parameter (and, hence, increase of dynamics) upon galactose binding across the protein.
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To understand the thermodynamic implications of the observed changes in dynamics, we employed the relationship between Lipari–Szabo order parameters \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tand conformational entropy derived by Yang and Kay (1996).
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In addition to generalised order parameters, we observed significant increase in backbone dynamics in the complex as compared with the apo protein by several other measures. RMS deviations from the average structure were significantly larger for the galactose-ABP complex than for apo-ABP. Fluctuations of backbone heavy-atom positions across the trajectory were generally larger in the complex than in the apo protein.
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As well as validating the experimental results, the MD simulations revealed details of dynamic changes in regions that could not be measured experimentally. Importantly, the simulations revealed complex changes in the dynamics of the ABP binding site: counter-intuitively, several residues in the binding site showed increase in flexibility upon binding, which was consistent with the trend seen throughout the rest of the protein. Other binding site residues displayed a decrease in flexibility, more in keeping with the intuitive expectation that ligand binding will reduce the conformational freedom of binding site residues (Figure 9).
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The total entropy alters due to changes in the pico- to nanosecond motion upon galactose binding, estimated from MD simulations and NMR results, was 610(± 120) J/mol K, which gives \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t188\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t±\n\t\t\t\t\t\t\t\t\t37\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t kJ/mol at 308 K. Clearly, this latter value is overestimated, as the assumption of un-correlated motion is not likely to hold for all residues of the protein. It is evident, however, that the entropy change associated with changes in \'fast\' dynamics contributes favourably to the free energy of binding. As the result, it allows the reduction of the unfavourable entropic contribution associated with ligand binding.
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Figure 9.
Backbone amide order parameters for apo-ABP and galactose-ABP complex. \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\torder parameters were obtained by NMR relaxation measurements (red dots) and MD simulations (black lines). Data obtained for apo-ABP are showed in panel (a), while results for galactose-ABP complex are displayed in panel (b). Panel (c) shows changes in order parameters induced by galactose binding to ABP, with protein secondary structure elements displayed above. The N-domain of ABP is coloured is yellow, the C-domain is coloured blue, and the hinge region is coloured red. Residues interacting with the ligand are coloured cyan. This colouring scheme is consistent with colouring in Figure 10. (figure taken from MacRaild et al., 2007)
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4.2.3. Origins of entropic costs of binding
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We investigated the origin of the large and unfavourable entropic contribution to the binding free energy of galactose-ABP, observed by ITC (Daranas et al., 2004), in terms of the different contributions.
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It is clear that formation of a ligand-protein complex will involve the loss of entropy associated with constraining the translational and rotational degrees of freedom of one binding partner with respect to the other. The magnitude of these unfavourable contributions to the ligand-protein interaction can be approximated. In the case of galactose-ABP interactions, we took an estimate of the loss of ligand translational and rotational entropy from the work by Turnbull et al (2004) and Lundquist and his coworkers (Lundquist and Toone, 2002), which gave at approximately 25 kJ/mol for the free energy penalty.
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It is assumed that the bound ligand will experience a loss of entropy reflecting the loss of conformational flexibility of the ligand in solution. On the assumption that conformational degrees of freedom are substantially restricted upon ligand-protein binding, the entropic penalty arising from loss in degrees of freedom of the galactose hydroxyl rotors alone is likely to be ~30 kJ/mol (Lundquist and Toone, 2002). Another contribution arises from the solvation effects. As calculated by means of QM/COSMO approach, desolvation energy of galactose is +87.6 kJ/mol (at 300 K), which is a significant unfavourable contribution (Bronowska and Homans, unpublished data).
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It is known that the ABP binding site contains a significant number of tightly bound water molecules, which maintain the structure of the binding site and play a role in governing the specificity of ligand binding (Quiocho, 1993). Examination of the structures of ABP in complex with its ligands (galactose, fucose, and arabinose) revealed some 15 crystallographically resolved and structurally conserved water molecules within the binding site (MacRaild et al., 2007). Dunitz (1994) estimated the maximal entropic cost of confinement of a single water molecule to 8 kJ/mol. Even if the cost of confinement of the water molecules in the binding site of ABP is lower than this maximal value, the overall cost of confining these water molecules in the binding site will be vast.
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All these amount to unfavourable entropic contribution of galactose-ABP binding. However, the entropy of galactose-ABP interactions is much lower than the sum of these contributions: as measured by ITC the \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t amounts to –61 kJ/mol at 308 K (Daranas et al., 2004). Observed discrepancy can be explained in terms of favourable contribution of protein dynamics to the entropy of galactose-ABP interactions, which was observed by NMR measurements and MD simulations.
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4.3. Enthalpy-entropy compensation revisited: bovine carbonic anhydrase II
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In studies on the thermodynamics of ligand-protein interactions, it is usually assumed that the bound ligand is fixed in the binding site. However, there is little direct experimental evidence for this assumption, and in the case of binding of p-substituted benzenesulfonamide inhibitors to bovine carbonic anhydrase II (BCA II), the observed thermodynamic binding signature assessed by ITC measurements leads indirectly to the conclusion that the bound ligands retain a considerable degree of flexibility (Krishnamurthy et al., 2006).
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BCAII and its binding to a large panel of ligands was reported in literature as a classic example of enthalpy-entropy compensation. Whitesides and his coworkers studied these interactions for a series of p-(glycine)n-substituted benzenesulfonamides (where n = 1-5) and found almost perfect enthalpy-entropy compensation across the series: as demonstrated by ITC measurements, the enthalpy of binding became less favorable and the entropy more favorable with increasing chain length. Changes in heat capacity were independent of chain length, which indicated that the observed changes in binding thermodynamic signatures across the series cannot be explained on the basis of the classical hydrophobic effect. In addition, strong evidence exists that these thermodynamic signatures are not driven by solvent effects (Syme et al., 2010). To explain the observed data, a model was proposed, which assumed the increased mobility of ligand upon the chain length growth. Such increased flexibility of the bound ligand (favourable entropic contribution) would be compensated by a decreased number of direct ligand-protein contacts (unfavourable enthalpic contribution).
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Such an increase in mobility of the ligand can be readily probed by 15N NMR relaxation measurements and computational studies. Thus, we investigated these using a combination of the solution NMR and MD simulations. Two series of ligands were studied: The first (series 1) consisted of six ligands with different chain lengths (n = 1-6), isotopically 15N -labeled at the terminal amide, whereas the second (series 2) comprised six ligands with the same chain length (n = 6), but isotopically 15N -labeled at a single amide at each position n. The ligands bound at the BCAII binding site are shown in Figure 10.\n\t\t\t\t
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Figure 10.
The binding of investigated benzenesulfonamides to BCAII. The protein backbone is coloured cyan, with three histidines (yellow) coordinating zinc atom (dark blue dot) displayed. The ligand is showed, with glycine side chains represented as VDW spheres and coloured as follows: n=1 – purple, n=2 – red, n=3 – orange, n=4 – bright yellow, n=5 – dark green, and n=6 – blue.
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Contrary to expectation, we found that the observed thermodynamic binding signature could not be explained by residual ligand dynamics in the bound state, but rather results from the indirect influence of ligand chain length on protein dynamics.
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Chemical shift changes on ligand binding were monitored by simple one-dimensional 1H, 15N HSQC spectra. It was possible, since each ligand was selectively 15N -labeled. The results (Figure 11) indicated that residues proximal to the aromatic moiety of ligand show substantial changes in chemical shift, whereas residues more distant to the aromatic moiety show much smaller changes. The former is indicative of interactions with the protein, while the latter suggests less substantive interactions with the protein by these more distant residues.
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Figure 11.
Observed chemical shift differences between free ligands and ligand-BCAII complexes, for both series of ligands, plotted against the number of glycine residues (n from 1 to 6) in the side chain (Stoeckmann et al., 2008).
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In order to obtain a more detailed picture of ligand dynamics in the bound state, 15N NMR relaxation data (R1, R2, NOE) were measured for each series of ligands. The results obtained showed that the ligand chain became more dynamic as n increased. However, there were substantial differences between series 1 and 2 ligands: series 1 ligands were much more dynamic for small n values, while in case of series 2 ligands, the three residues nearest the aromatic ring adopted slow dynamic motions, whereas the three residues distal to the aromatic ring adopted faster dynamics similar to series 1 ligands.
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The relaxation data were analysed using the Lipari-Szabo model-free approach (1982) and the \'fast\' dynamics was quantified by means of generalised order parameters. The entropic contributions arising from these \'fast\' motions were assessed from \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t parameters using the relation derived by Yang and Kay (1996).
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The order parameters of all ligand Gly residues in both series were much smaller than those of backbone residues within protein, indicating a comparatively high mobility of the ligand chain. The first two residues of the ligand were relatively immobile adjacent to the aromatic ring and are engaged in direct interactions with the protein, whereas the last four Gly residues were significantly more mobile, with \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t values indicating motions being unrestricted by BCAII protein. From \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t values, the entropic contribution to ligand-BCAII binding arising from each Gly residue, could be obtained. These data are shown in Figure 12.
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Figure 12.
Top panel: order parameters for backbone amide (N-H) bond vectors for ligand series 1 (white) and 2 (black), obtained from NMR relaxation measurements. Middle panel: Derived entropy differences for series 1 ligands (white) compared to entropies of binding obtained from ITC measurements (black) (Krishnamurthy et al., 2006). Bottom panel: Entropy differences for series 2 ligands (per N-H bond vector), obtained by NMR.
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Comparing these results with ITC data by Krishnamurthy et al. (2006), it is clear that a poor correlation exists between the change in ligand conformational entropy determined from NMR relaxation studies and the entropies of binding derived from ITC (Figure 14, middle panel). It indicates that a model based on increased dynamics of the ligand in the bound state is not a plausible explanation for the observed thermodynamic binding data. This is not entirely unexpected since the ITC values are global parameters, which include contributions not only from the ligand, but from protein and solvent as well. However, the role of solvation is unlikely to be the driving one in the case of ligand-BCAII binding – for three reasons. First, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tΔ\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tvalues for the interaction determined by ITC are independent of Gly chain length (Stoeckmann et al., 2008). Second, these values are fairly small: around 80 J/mol/K. Finally, ligands are not fully desolvated upon the binding event: more distal residues extend beyond the binding pocket and they interact with water molecules. The observed increase in entropy with respect to the ligand chain length is approximately linear, which argues against a significant solvation contribution.
\n\t\t\t\t
It was hoped that assessment of the protein contribution would shed light on the observed binding signature. To achieve this, MD simulations of both series of ligands in complexes with BCAII were performed (Stoeckmann et al., 2008). In order to validate the methodology, generalised order parameters for ligand amide vectors were calculated from the trajectory and compared to NMR data. These MD trajectories were then used to probe the influence of ligand binding on protein dynamics. Specifically, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tS\n\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tvalues for backbone amide bond vectors, side chain terminal heavy-atom bond vectors, and corresponding conformational entropies were calculated for each complex with series 1 ligands.
\n\t\t\t\t
The results obtained showed that the aromatic moiety became correspondingly more rigid with respect to series 1 ligand chain length. This was consistent with the NMR data showing that addition of successive glycine residues decreased the dynamics of the preceding units. Moreover, we observed the trend of increased dynamics of protein residue side chains with respect to ligand chain length (Table 2). This counter-intuitive observation that ligand binding increases protein dynamics has been observed in a number of ligand-protein systems, including ABP, which was described in the previous section of this chapter.
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Residues
\n\t\t\t\t\t\t\t
Gly2-Gly1
\n\t\t\t\t\t\t\t
Gly3-Gly2
\n\t\t\t\t\t\t\t
Gly4-Gly3
\n\t\t\t\t\t\t\t
Gly5-Gly4
\n\t\t\t\t\t\t\t
Gly6-Gly5
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Biding site
\n\t\t\t\t\t\t\t
4.37 ± 1.1
\n\t\t\t\t\t\t\t
5.28 ± 1.2
\n\t\t\t\t\t\t\t
4.33 ± 1.0
\n\t\t\t\t\t\t\t
3.11 ± 1.0
\n\t\t\t\t\t\t\t
6.04 ± 1.3
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Whole protein
\n\t\t\t\t\t\t\t
14.9 ± 1.7
\n\t\t\t\t\t\t\t
4.6 ± 1.8
\n\t\t\t\t\t\t\t
5.5 ± 2.2
\n\t\t\t\t\t\t\t
9.9 ± 2.4
\n\t\t\t\t\t\t\t
8.4 ± 2.5
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 2.
Differences in per-residue entropies quantified as TDS (in kJ/mol at temperature 300 K) for residues in the binding pocket of BCAII as well as for the whole BCAII protein. Displayed differences are result of changing side chain length of the ligand (Glyn – Glyn-1).
\n\t\t\t\t
Summarising, our results suggest that the enthalpy-entropy compensation observed for binding of ArGlynO- ligands to BCA II derives principally from an increase in protein dynamics, rather than ligand dynamics, with respect to the ligand chain length. Krishnamurthy and his coworkers showed that enthalpy-entropy compensation was observed for a range of BCAII ligands, whose structurally distinct chain types gave similar thermodynamic signatures (Krishnamurthy et al., 2006). This suggests that a common process is underway that is unlikely to be related to specific interactions between the chain and the protein. In our study, we demonstrated an increase in protein dynamics upon binding longer-chained ligands. This observation provides an explanation for the enthalpy-entropy compensation across these structurally distinct ligands.
\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
5. Conclusions
\n\t\t\t
The notion of the binding event being the result of shape complementarity between ligand and protein binding site (key-and-lock model) has been a paradigm in the description of binding events and molecular recognition phenomena for a long time. The recent discovery of the important role played by protein dynamics and solvent effects, as well as the enthalpy-entropy compensation phenomenon, challenged this concept, and demanded the thorough examination of entropic contributions and solvent effects. Assessment of all these contributions to the thermodynamics of ligand-protein binding is a challenging task. Although understanding the role of each contribution and methods allowing for a complete dissection of thermodynamic contributions are tasks far from being completed, significant progress has been made in recent years. For instance, development of high-resolution heteronuclear NMR methods allowed for assessment of the contribution from protein degrees of freedom to the intrinsic entropy of binding. The usefulness of such approach has been demonstrated in the course of this chapter on several ligand-protein examples. In addition, progresses in the development of MD-related methodologies and advanced force fields enabled the application of the NMR-derived formalism on relevant time scales and the assessment of the intrinsic entropic contributions solely using computational methods. Development of QM methods allows the study of larger and larger systems, while advances in ITC calorimetry allow the use of very small amounts of reagents for a single experiment.
\n\t\t\t
Despite this progress, much remains to be done. The enthalpy-entropy compensation phenomenon seems to be widespread among ligand-protein systems. It seems universal: binding restricts motions, while motions oppose tight confinement. However, our current knowledge about intrinsic protein dynamics is still insufficient to allow us to predict this phenomenon and hence to exploit it for the purposes of rational molecular design. Another challenge lies within the quantification of solvation contributions. There seem to be conflicting data regarding the contributions from confined water molecules. Their influence on binding can be favourable or unfavourable, enthalpy- or entropy- driven. Bound water molecules can be released upon ligand binding or – on the contrary – bind tighter (Poornima CS and Dean, 1995a-c). Their presence can make the protein structure more rigid (Mao et al., 2000), or more flexible (Fischer and Verma, 1999). Finally, protein binding sites can be fully solvated prior to binding, or fully desolvated (Barratt et al., 2006, Syme et al., 2010). The only common feature that seems to exist is that the contribution of the solvation effects to the ligand-protein binding thermodynamics can be – and often is – significant.
\n\t\t\t
Last but not least, intrinsic entropic contributions are notoriously difficult to quantify. A handful of experimental and theoretical methods can be employed to quantify these contributions, as have been described. However, all of these methods have their limitations, and one should be aware of these and of the assumptions that are being made. Theoretical results should be treated with caution, experimental data likewise, as they are based on many approximations and heavily dependent on the conditions applied. Care must be taken not to over-extrapolate data, and not fall the victim to confirmation bias.
\n\t\t\t
Fundamentally, in order to predict free energy of binding accurately, it would be necessary to go beyond predicting a single \'dominant\' conformation of the ligand-protein complex. It should be emphasised that the overall shape of the free energy landscape controls the binding free energy. This shape is affected by the depth and width of the local minima, and the height and breadth of the energy barriers. The factors that shape that landscape include intrinsic entropic contributions of both interacting partners, ligand poses, protein conformations, solvent effects, and protonation states. Computational and experimental approaches combined together can provide insight into this crucial but otherwise hidden landscape, which is pivotal not only to understand the origin of each contribution and its role in the binding event, but which can allow a truly rational molecular design.
\n\t\t
\n\t
Acknowledgments
\n\t\t\t
I would like to thank my collaborators and coauthors of my publications: Steve Homans, Chris MacRaild, Arnout Kalverda, Liz Barratt, Bruce Turnbull, Antonio Hernandez Daranas, Neil Syme, Caitriona Dennis, Dave Evans, Natalia Shimokhina, Pavel Hobza, Jindra Fanfrlik, Honza Rezac, Honza Konvalinka, Jiri Vondrasek, Jiri Cerny, Henning Stoeckmann, Stuart Warriner, Rebecca Wade, and Frauke Gräter. I also would like to thank for the financial support: BBSRC (United Kingdom), DAAD (Germany), DFG (Germany), Heidelberg Institute for Theoretical Sciences, and University of Heidelberg, Germany.
\n\t\t
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/21844.pdf",chapterXML:"https://mts.intechopen.com/source/xml/21844.xml",downloadPdfUrl:"/chapter/pdf-download/21844",previewPdfUrl:"/chapter/pdf-preview/21844",totalDownloads:17877,totalViews:1510,totalCrossrefCites:10,totalDimensionsCites:59,totalAltmetricsMentions:0,introChapter:null,impactScore:21,impactScorePercentile:99,impactScoreQuartile:4,hasAltmetrics:0,dateSubmitted:"November 3rd 2010",dateReviewed:"April 18th 2011",datePrePublished:null,datePublished:"November 2nd 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/21844",risUrl:"/chapter/ris/21844",book:{id:"297",slug:"thermodynamics-interaction-studies-solids-liquids-and-gases"},signatures:"Agnieszka K. Bronowska",authors:[{id:"34995",title:"Dr.",name:"Agnieszka",middleName:"K.",surname:"Bronowska",fullName:"Agnieszka Bronowska",slug:"agnieszka-bronowska",email:"bronka@tiger.chem.uw.edu.pl",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Heidelberg University",institutionURL:null,country:{name:"Germany"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Principles",level:"1"},{id:"sec_2_2",title:"2.1. Enthalpic and entropic components of free binding energy",level:"2"},{id:"sec_3_2",title:"2.2. Specific interactions",level:"2"},{id:"sec_3_3",title:"2.2.1. Electrostatic interactions ",level:"3"},{id:"sec_4_3",title:"2.2.2. Van der Waals interactions",level:"3"},{id:"sec_5_3",title:"2.2.3. Hydrogen bonds",level:"3"},{id:"sec_6_3",title:"2.2.4. Halogen bonds and multipolar interactions",level:"3"},{id:"sec_7_3",title:"2.2.5. Hydrophobic interactions",level:"3"},{id:"sec_8_3",title:"2.2.6. Interactions mediated by aromatic rings",level:"3"},{id:"sec_10_2",title:"2.3. Solvent effects, structural waters, and the bulk water",level:"2"},{id:"sec_11_2",title:"2.4. Classical and non-classical hydrophobic effect",level:"2"},{id:"sec_12_2",title:"2.5. Enthalpy-entropy compensation, binding cooperativity, and protein flexibility",level:"2"},{id:"sec_14",title:"3. Methods",level:"1"},{id:"sec_14_2",title:"3.1. Experimental methods",level:"2"},{id:"sec_14_3",title:"3.1.1. Isothermal titration calorimetry (ITC)",level:"3"},{id:"sec_14_4",title:"3.1.1.1. Experimental setup",level:"4"},{id:"sec_15_4",title:"3.1.1.2. Thermodynamic content of ITC data",level:"4"},{id:"sec_15_5",title:"3.1.1.2.1. Enthalpic contributions",level:"5"},{id:"sec_16_5",title:"3.1.1.2.2. Entropic contributions",level:"5"},{id:"sec_17_5",title:"3.1.1.2.3. Enthalpy-entropy compensation",level:"5"},{id:"sec_20_3",title:"3.1.2. Nuclear Magnetic Resonance (NMR) spectroscopy",level:"3"},{id:"sec_20_4",title:"3.1.2.1. Slow and fast dynamics: from dynamics to entropy",level:"4"},{id:"sec_22_3",title:"3.1.3. Combination of ITC and NMR ",level:"3"},{id:"sec_24_2",title:"3.2. Computational approaches",level:"2"},{id:"sec_24_3",title:"3.2.1. Molecular Dynamics (MD) simulations",level:"3"},{id:"sec_25_3",title:"3.2.2. Quantum mechanical (QM) calculations",level:"3"},{id:"sec_28",title:"4. Examples",level:"1"},{id:"sec_28_2",title:"4.1. Hydrophobic versus hydrophilic binding pocket: MUP and HBP",level:"2"},{id:"sec_28_3",title:"4.1.1. Structures of HBP and MUP",level:"3"},{id:"sec_29_3",title:"4.1.2. Calorimetric studies of MUP",level:"3"},{id:"sec_30_3",title:"4.1.3. Calorimetric studies of HBP",level:"3"},{id:"sec_31_3",title:"4.1.4. NMR relaxation measurements",level:"3"},{id:"sec_32_3",title:"4.1.5. Molecular dynamics (MD) simulations",level:"3"},{id:"sec_33_3",title:"4.1.6. Solvation thermodynamics estimation",level:"3"},{id:"sec_34_3",title:"4.1.7. Driving forces for ligand binding by MUP and HBP ",level:"3"},{id:"sec_36_2",title:"4.2. An integrated picture of galactose binding to Arabinose Binding Protein (ABP)",level:"2"},{id:"sec_36_3",title:"4.2.1. NMR measurements of ABP",level:"3"},{id:"sec_37_3",title:"4.2.2. Molecular dynamics (MD) study",level:"3"},{id:"sec_38_3",title:"4.2.3. Origins of entropic costs of binding",level:"3"},{id:"sec_40_2",title:"4.3. Enthalpy-entropy compensation revisited: bovine carbonic anhydrase II",level:"2"},{id:"sec_42",title:"5. Conclusions ",level:"1"},{id:"sec_43",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAlves\n\t\t\t\t\t\t\tI. D.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPark\n\t\t\t\t\t\t\tC. K.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHruby\n\t\t\t\t\t\t\tV. 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1. Introduction
Humic substances are principal compounds that account for 80–90% of soil organic matter [1]. Owing to their complexes of bioactive substances, humic substances can control the stability and ecosystem in soil [2]. Humic substances are organic macromolecules with multiple properties and contain a wide variety of structural functional groups [3], arising from physical, chemical, and microbiological processes [4]. In aquatic system, humic substances account for 40–60% of natural organic matter [5]; thus, they can have significant impact on water quality. In natural water resources, humic substances are formed from the degradation of plants, animal residues, and soil surface runoff [6]. In the events of torrential rainfall, storm events, and summer monsoon season, humic substances are abundantly formed from upstream land use and soil surface runoff [7, 8, 9]. Thus, it is essential to study the humic substances extracted from upland soils for water quality management.
Humic substances are mainly divided into humic acids, fulvic acids, and humin [10]. Humic acids are soluble in water only at pH >2, fulvic acids are soluble in water in all pH conditions, whereas humin is insoluble in water [11]. Thus, because of their solubility, humic and fulvic acids play indispensable roles in dissolving organic matter in aquatic system. Humic and fulvic acids majorly comprise carboxylic, phenolic, carbonyl, hydroxyl, amine, amide, and aliphatic moieties [12]. In surface water, fulvic acids account for the majority of humic substances, whereas humic acids account for only 10% of humic substances [6]. For the disinfection of water for drinking purpose, humic and fulvic acids present in water can react with disinfectant chemicals (i.e., chlorine and ozone) to form disinfection by-products (DBPs) such as trihalomethanes (THMs), haloacetic acids (HAAs), haloketones, and haloacetonitriles [13, 14]. Among these four DBPs, THMs and HAAs are the two most abundant halogenated DBPs [15, 16]. DBPs are considered to be dangerous to human health because of the presence of potential carcinogens [17] that particularly cause urinary bladder cancer [18, 19]. Humic and fulvic acids are the primary sorbent, which can impact the fate, mobility, and bioavailability of hydrophobic contaminants, especially the presence of polycyclic aromatic hydrocarbons (PAHs) in water system [20, 21]. PAHs contain more than two benzene rings [22] and are the most persistent and toxic organic micropollutants in surface water. Low concentrations of these hydrocarbons can have adverse effects on human health and aquatic systems because they contain carcinogenic, mutagenic compounds and potent immune suppressants [23, 24]. PAHs can be formed from biological process, industrial wastes, petroleum spills, incomplete combustion from nature sources (forest and brush fires), and/or human combustion sources (engine emissions) [24]. Recently, PAHs from urban runoff were reported to be a serious contaminant in rivers and lakes [9, 25, 26]. In addition, PAHs have been widely detected in surface water and drinking water at higher concentrations compared with other persistent organic pollutants [27, 28]. Of the 16 PAHs monitored by the US Environment Protection Agency, phenanthrene (PHE) was reported to be the most abundant PAH in surface water. Similar to humic and fulvic acids, PAHs can produce chlorinated PAHs during chlorination process for drinking water treatment. This is because PAHs contain an electronic-rich system that can be readily attacked electrophilically by hypochlorous acid [29]. The hypobromous acid might also be formed in the presence of Br− during chlorination because Br− ion is ubiquitous in both surface water and chlorine solution [30]. The hypobromous acid reacts with PAHs to form brominated PAHs. In comparison with PAHs, the chlorinated PAHs and brominated PAHs exhibit AhR activity, DNA damaging effects, and mutagenicity, and thus, they present a larger threat to human health [31, 32]. Hence, it is essential to investigate the characteristics of humic and fulvic acids and their PAHs sorption behavior in order to control the formation of DBPs from humic and fulvic acids as well as the formation of halogenated PAHs.
Many methods to identify the structure of humic and fulvic acids have been reported in the literature. Among them, 13C NMR is the most common method to identify functional groups and molecular structures such as aromatic and aliphatic C groups. In contrast, elemental analysis, which is a faster method than the 13C NMR method, reflects the atomic ratios that relate to aromatic C such as H/C and the derived sources of humic substance such as N/C [33]. The humic and fulvic acids can also be compared on the basis of molecular weight (MW) [34]. In addition, spectroscopic techniques such as UV–visible (Vis) absorbance and fluorescence are widely employed because of their simplicity, rapid process, and non-requirement of pretreatment of samples. Specific UV absorbance (SUVA) at 254 nm is an indicator for aromatic C, while their ratio E4/E6, S275–295, and S350–400 confirmed its humification and aromaticity [35]. Moreover, the sources of humic substance and its relationship with MW distribution humic substance can be determined using fluorescence properties and their index [36]. Although many studies have examined the characteristics of humic substances and their binding with PAHs, there is still a lack of sufficient information on the characteristics of humic and fulvic acids and their differences, formation of their DPBs, and binding behavior of their PAHs.
In this present study, humic and fulvic acids were extracted from soils and characterized by using 13C NMR, elemental analysis, MW, UV, and fluorescence methods. The study also reported the different formation of THMs and HAAs and the PHE behavior of humic and fulvic acids due to their different characteristics.
2. Study sites and characterization methods
2.1 Soil sampling sites and extraction method
For this study, soil samples were collected from six different locations in Korea, and a minimum of 20 km distance was maintained between the sampling sites. Four of these samples were representative of granite soils and were named as Gori (KR), Wolseong (WS), Uljin (UJ), and Yeonggwang (YK). Two soil samples were collected from the foot of Mt. Seorak (Goseong (KS)) and from Mt. Hallan (Jeju Island, volcanic ash soil) (Halla (HL)). Three soil samples, namely Elliott Silt Loam Soil (Cat No. 1BS102M), Canadian peat moss soil sample (sphagnum peat moss), and Aldrich HA (Sigma-Aldrich, CAS no. 1415-93-6), were purchased and named as IHSS, Peat, and AL, respectively.
Humic and fulvic acids were extracted from six sampling soils (KR, WS, UJ, YK, KS, and HL) and Peat, according to the IHSS method [37] and ISO 12782-4:2012 [38]. The extracted fulvic acids were purified using XAD resin concentration and then passed through a Dowex-50X8(H+) column. The AL sample was purified using an acid–base precipitation method [10]. Figure 1 presents the extraction and purification process of humic and fulvic acids.
Figure 1.
The extraction and purification process of humic and fulvic acids.
2.2 Characterization methods
The UV–Vis absorbance of humic and fulvic acids in the 200–800 nm range was investigated using a UV–Vis spectrometer (Shimadzu, UV-1601PC). To measure the dissolves organic matter (DOC), samples were prepared at pH 7.0 and then filtered using a 0.45-μm membrane filter (cellulose acetate, Advantec). The ratio of UV absorbance at 254 nm to the DOC concentrations of the samples was calculated to determine the SUVA values. The UV–Vis absorbance ratio at 465 and 665 nm (E4/E6) and spectral slope were applied to characterize the humic material as well as the aromaticity. The spectra slope was calculated using log-transform linear regression at intervals of 275–295 nm (S275–295) and 350–400 nm (S350–400). These two narrow bands were chosen for spectral slope calculation because they present the greatest variations from a variety of sources (i.e., marsh, riverine, estuarine, coastal, and open ocean).
A fluorescence spectrometer (Perkin Elmer LS50B) was used to obtain synchronous fluorescence spectra. It is known that fluorescence intensity can alternate with measurement time depending on external conditions such as humidity. Thus, the measured fluorescence intensities were normalized as units of quinine sulfate (QSE) equivalents based on the fluorescence of a diluted series of quinine sulfate dehydrate in 0.05 M sulfuric acid at an excitation/emission wavelength of 350/450 nm. Both the excitation and emission slits were fixed at 10. The difference between the emission wavelength and the excitation wavelength (Δλ) was fixed at 30 nm and then measured from 250 to 600 nml to determine synchronous fluorescence spectrum. The relative fluorescence regions were classified into four groups: protein-like (%PLF) fluorescence, fulvic-like (%FLF) fluorescence, humic-like (%HLF) fluorescence, and terrestrial humic-like (%THLF) fluorescence. These groups of fluorescence regions corresponded to the relative percentage of fluorescence intensity at wavelengths of 250–300, 300–380, 380–420, and 420–600 nm, respectively.
The apparent weight-average molecular weight (MWw) values were determined using size exclusion chromatography. The polydispersity of samples with the relative precision of MWw and MWn were less than 5% and 7%, respectively. The elemental composition ratios of humic and fulvic acids (C/H, N/C, and (N + O)/C) were determined using CHNS-932 and VTF-900 (LECO Co.).
The cross-polarization magic-angle spinning method was used to determine carbon structure via 13C NMR spectroscopy (Bruker Avance II, 500 MHz). The spectrum was measured at 300 K with a 90-pulse width of 4.5 s, 1.5 ms contact time, 3 ms pulse delay time, and 6.0 kHz spinning speed. A qualitatively good signal-to-noise ratio was obtained by using a total of 3 × 104 scan signal free induction decays and a line broadening function of 40 Hz. Then, the C functional groups were determined by integrating the area of the spectra in the chemical shift area: 0–50 ppm (alkyl C), 50–110 ppm (O-alkyl C), 110–145 ppm (C,H-aryl), 145–165 ppm (O-aryl phenol), and 165–190 ppm (carboxyl).
2.3 Chlorination of humic and fulvic acids and THMs/HAAs measurement
Humic and fulvic acids extracted from six sampling soils (KR, WS, UJ, YK, KS, and HL), Peat, AL, and IHSS were diluted to 1 mg C/L. Then, 1 ml phosphate buffer was added to 50 ml of diluted humic and fulvic acids to adjust their pH value to 7.0 ± 0.2. Then, the humic and fulvic acids were incubated for 2–3 h before chlorination. Chlorination of the humic and fulvic acids was conducted using the Aldrich’s sodium hypochlorite solution (available chlorine >4%) (NaOCl). The glassware required for the experiment was washed with acetone and then baked at 400°C for 1 h to remove any remaining organic matter. A constant dose of 5 mg Cl2/L was added to each sample for chlorination. The final solutions were sealed and stored in the dark at 25°C for 24 h. Then, a 10% sodium sulfite solution was injected into the solutions to suppress the formation of additional by-products by residual chlorine.
THMs and HAAs were analyzed using USEPA Method 551.1 and Method 552.3. A micro-electron capture detector (Agilent 6890 GC-ECD) was used to conduct gas chromatography of the liquid–liquid extracts in order to quantify the different THMs and HAAs. This was followed by diazomethane derivatization. Four species of THM, i.e., such as chloroform (CF), dichlorobromomethane (DCBM), dibromochloromethane (DBCM), and bromoform, were measured. HAAs were analyzed using the Drinking Water Quality Process Test Method (ES 05552.2.). Three substances, i.e., dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), and dibromoacetic acid (DBAA), were used in this analysis.
2.4 PHE adsorption experiment and analysis
PHE (purity >97%) was purchased from Aldrich and used without further purification. The stock solution (1.0 mg/L) was prepared by dissolving an excess in methanol to make a saturated solution. This solution was filtered through a 0.45-μm cellulose acetate membrane filter (Advantec). In this filter, the sorption of PHE is negligible. The solution was sterilized by adding 5 mM CaCl2 and 0.01 mM HgCl2, and its pH was adjusted to 6 by adding either 0.1 M NaOH or 0.1 M HCl. Then, 100, 90, 80, 70, 60, 40, and 30% stock solution was used with 20 mg of humin added in a 10-mL glass vial in order to perform the PHE adsorption experiments. The headspace was kept minimal to reduce the solute vapor loss and minimize the effect of surface adsorption. In addition, separately manufactured vial caps were used. The experiment was conducted using a rotator (at 30 rpm). Based on the preliminary tests for apparent equilibrium, the reaction time was set to 5 d. After the reaction, a centrifuge (5000 rpm, 15 min) was used to separate the supernatant and precipitate from each sample. The HPLC (YoungLin, UV730D) was used to measure PHE concentration in the supernatant. The mobile phase for HPLC was prepared using acetonitrile and ultrapure water (80:20 v/v) with a flow rate of 1.8 mL/min. A C18 4.6 × 150 mm reverse-phase column (Supelcosil LC-18DB) was used to perform separation analysis using a UV detector (at 254 nm).
A modified Freundlich adsorption isotherm Eq. (1) was used to analyze the adsorption results. The Freundlich equation is related to multi-layer and heterogeneous adsorption and is, thus, commonly applied to organic matter and hydrophobic pollutant adsorption [39]. The Freundlich adsorption constant (KFOC) and isotherm linearity constant (n) were derived from the slope and y-intercept, respectively, as per Eq. (1):
SOC=KFOC×Ce/CsclnE1
where SOC is the concentration of the PHE adsorbed on the humin (μg/kg C), Ce is the freely dissolved PHE concentration (μg/L), Cscl is the supercooled solubility of PHE at 25°C (5970 μg/L) in supercooled aqueous solution, and KFOC (μg/kg C) and n are the Freundlich adsorption model parameters (adsorption isotherm linearity increases as n increases). The single-point sorption is as follows:
KOC=SOCCi=KFOC×Cin−1/CsclnE2
From Freundlich sorption coefficient, the Gibbs energy change (△G) can be calculated as follows [40]:
ΔG°=−RTlnKE3
where T is the absolute temperature in kelvins, R is the gas constant (8.314 J.mol−1.K−1), and K is the Freundlich adsorption coefficient (KOC). The K value is recalculated as a dimensionless coefficient by multiplying it by 55.5 (number of moles of water per liter of solution) to correct the △G° values [41]:
ΔG°=−RTln55.5KE4
3. Characteristics of humic and fulvic acids
The humic acids exhibited higher values of elemental composition of H/C and N/C ratios compared with those of fulvic acids for almost samples (Figure 2). Fulvic acids exhibited higher values of (N + O)/C and, thus, presented higher polarity than those values for humic acids (Figure 2). Similar to previous research works, aromatic C (110–165 ppm) in humic acids was higher than that in fulvic acids, whereas the aliphatic C (0–110 ppm) exhibited the opposite trend (Figure 3). In addition, only O-alkyl C proportions in fulvic acids presented higher values compared with those in humic acids for all soil samples, except for Peat and HL soil. In contrast, O-alkyl C, C,H-aryl C, and O-aryl C exhibited the opposite trend. Moreover, the carboxyl and carbonyl groups (165–210 ppm) in fulvic acids were higher compared with the values in humic acids for all soil samples. Higher aromatic C compounds (C,H-aryl and O-aryl phenol) in humic acids indicated higher amount of lignin and polyphenol from various plants [42]. In addition, the presence of higher O-alkyl and carboxyl groups such as peptides and organic acids indicated higher solubility of humic acids compared with fulvic acids [43]. Fulvic acids contain high carboxyl groups, and hence, the appearance of both COOH and –HC=CH– structure in these acids might affect the result of H/C [44]. Thus, fulvic acids exhibited lower H/C ratios as compared to the values of these ratios in humic acids. The MWw values of humic acids ranged from 2545 to 4411 Da and were higher than the values of fulvic acids (from 1751 to 2584 Da). In comparison with fulvic acid, humic acids presented higher polydispersity index (MWw/MWn), revealing a larger distribution of MW for humic acids. Thus, humic acids extracted from soils revealed higher H/C (affected by COOH and –HC=CH– structure), N/C, MWw, and MWw/MWn and lower polarity and O-alkyl C compared with fulvic acids. In particular, fulvic acids contained higher aliphatic C owing to their extremely higher values of O-alkyl C.
Figure 2.
Atomic ratios of humic acids (HA) and fulvic acids (FA) extracted from soils.
Figure 3.
13C NMR spectra of (a) humic acids (HA); and (b) fulvic acids (FA) extracted from soils.
SUVA, E4/E6, S275–295, and S350–400 were employed to identify the difference in spectroscopic spectra between humic and fulvic acids. Humic acids presented higher SUVA values than those of fulvic acids, whereas E4/E6 exhibited the opposite trend. The negative relationship between E4/E6 and the degree of condensation of the aromatic carbon network and/or the MW has facilitated the increased usage of the E4/E6 ratio in the identification of humification and aromaticity of soil organic matter [35, 45, 46]. The lower values of E4/E6 for humic acids are associated with the higher values of SUVA, aromatic carbon (110–165 ppm), and MWw. Of all soil samples, S350–400 presented higher values of SUVA, aromatic carbon (110–165 ppm), and MWw for fulvic acids than for humic acids, whereas the soil sample S275–295 did not present any trend. Thus, spectra slope at longer wavelengths could be used as an effective index to distinguish the dissolved organic matter between humic acids and fulvic acids.
Figure 4 shows the synchronous fluorescence spectra of soil humic acids and fulvic acids. As shown in this figure, soil humic acids presented higher peaks at THLF regions, whereas fulvic acids exhibited lower peaks at FLF and HLF regions. With respect to fluorescence relative distribution, compared with fulvic acids, humic acids presented lower %FLF and %HLF, but higher %THLF values. The humification index (HIX) also presented higher values for humic acids than for fulvic acids. Based on these findings, it can be said that soil humic acids were more condense with polymerized humic-like structure (higher SUVA, aromatic C, MWw, and %THLF), whereas fulvic acids contained high levels of carbonyl and quinone, aliphatic groups, and oxygen functional groups related to fulvic- and humic-like fluorescence materials (higher (N + O)/C, E4/E6, S350–400, %FLF, %HLF O-alkyl, and carboxyl groups). These specific different molecular characteristics between humic and fulvic acids are important as they can aid in investigating the structural changes, generation of DBPs under chlorination, and PHE sorption behavior.
Figure 4.
Fluorescence spectra of (a) humic acids*; and (b) fulvic acids extracted from soils. *Figure 4a was presented in research [20].
4. DPBs formation and the structural changes of humic and fulvic acids extracted from soils
4.1 DBPs formation of humic and fulvic acids extracted from soils
For the comparison of DBPs formation between humic and fulvic acids extracted from soils, the concentrations of THM, HAA, and their species (μg/L) were normalized to DOC (mg/L) and named as specific THM/HAA formation potential (STHMFP/SHAAFP) and CF, DCBM, DBCM, DCAA, TCAA, and DBAA. It was found that the formation of STHMFP and SHAAFP significantly differed between humic acids and fulvic acids. Humic acids exhibited higher STHMFP values that ranged from 141.9 to 194.6 μg/mg compared with the lower values in fulvic acids (98.7 to 100.6 μg/mg). In both humic acids and fulvic acids, chloroform was the most dominant compound of STHMFP, accounting for over 95% of STHMFP. Similar to the trend of the values of STHMFP, SHAAFP values for humic acids were found to be 5.5 times higher (ranging from 259.0 to 390.0 μg/mg) compared with the SHAAFP values for fulvic acids (ranging from 19.8 to 54.9 μg/mg). TCAA was the most abundant (83%) SHAAFP species in humic acids, whereas it accounted for only 17% in fulvic acids. In humic acids, SHAAFP (presenting as TCAA) presented higher values than STHMFP (presenting as CF). In contrast, STHMFP presented much higher values than SHAAFP for fulvic acids. During chlorination, humic molecules having higher aromatic content, such as humic acids, first reacted with chlorine to form TCAA and then form CF. This is the reason why TCAA production was higher than CF production [47]. In addition, fulvic acids contained alkyl, carbohydrate, and carboxyl groups that were less capable of producing CF and TCAA under chlorination than the aromatic carbon components [48]. Thus, the difference in DPB formation between humic and fulvic acids might be explained by the differences in their molecular structure.
4.2 Changes in spectroscopic characteristics and TOC and their relationship after chlorination
After chlorination, both humic and fulvic acids exhibited lower SUVA and TOC values. TOC removal presented higher values for fulvic acids. This demonstrated the presence of more organic carbon components in fulvic acids that can be easily mineralized to CO2 during chlorination as compared to humic acids. During chlorination, 50–80% of chlorine oxidated humic substances into CO2, whereas only 5–10% of chlorine participated in the incorporation reaction to form DBPs [49]. SUVA presented higher reduction values for humic acids than for fulvic acids. This indicated that high aromatic C (presented by high SUVA values) were transformed into lower ones (lower SUVA) by splitting the aromatic rings and decomposing the unsaturated carbon rather than converting it into CO2. After chlorination, humic and fulvic acids showed different changes in terms of fluorescence relative distribution. Moreover, after chlorination, only %THLF values decreased for humic acids, whereas %FLF and %THLF reduced for fulvic acids. The values of Δ%FLF, Δ%HLF, and Δ%THLF were calculated based on the differences in each relative distribution before and after chlorination (FLbefore − FLafter). These values were used to further examine the mechanisms of chlorination for humic and fulvic acids.
The relationship between the changes in humic/fulvic acid characteristics (TOC removal, SUVA removal, Δ%FLF, Δ%HLF, and Δ%THLF) due to chlorination and their original structural characteristics (SUVA, MW, C,H-aryl, and O-alkyl) can clarify the chlorination-induced structural changes in humic and fulvic acid in more depth. SUVA values were found to be strongly positively correlated with SUVA removal for both humic and fulvic acids (p < 0.05). Moreover, SUVA values were strongly negatively correlated with TOC removal values for humic acids (p < 0.01) and non-significantly correlated with TOC removal values for fulvic acids (p > 0.1). The higher SUVA values and aromatic carbon contents of humic acids than those of fulvic acids resulted in the reaction of more aromatic carbon compounds in humic acids with chlorine. This led to the production of higher DBP. In addition, for humic acids, higher MWw materials with richer N groups and a higher proportion of O-alkyl C reacted with chlorine to yield higher reductions of TOC and FLF components. In comparison, lower MWw materials with a higher percentage of C,H-aryl and O-aryl phenol and fewer N groups were associated with a higher reduction in SUVA and THLF values. In other words, high-MWw humic acids with aliphatic properties, high nitrogen content, and a low degree of unsaturation mostly reacted with chlorine via an oxidation reaction. In comparison, low-MWw humic acids with high aromatic C and low nitrogen content mainly reacted with chlorine via incorporation.
4.3 Specific relationship between DPBs and humic and fulvic acid characteristics
The correlations between STHMFP and humic/fulvic acid characteristics determined the effects of the molecular structures of humic/fulvic acids on DBP production. For humic acids, STHMFP was positively correlated with C,H-aryl, O-aryl phenol, and alkyl C (p < 0.05) but negatively correlated with N/C ratio, MWw, TOC removal, Δ%FLF, O-alkyl C, and carboxyl (p < 0.05). In summary, low-MW aromatic C such as C,H-aryl, O-aryl phenol, and aliphatic C such as alkyl C in humic acids were considered as DBP precursors as they generated STHMFP during the chlorination reaction. On the contrary, high N groups of O-alkyl C in humic acids participated in oxidation reactions during chlorination. For fulvic acids, STHMFP presented a strong positive correlation with alkyl C and negative correlations with C,H-aryl and O-aryl phenol (p < 0.05). The chlorination mechanism of fulvic acids shown in our study differed from those reported in previous studies that demonstrated that aromatic C, especially phenol components, were associated with the highest value of STHMFP [50, 51]. The reasons behind these notable findings can be explained as follows. First, unlike those in the humic acids, the aromatic contents in fulvic acids might not play a major role in the generation of STHMFP via incorporation. Second, the presence of N groups combined with aromatic molecules in the fulvic acids resulted in the formation of N-DBPs, and not in the generation of STHMFP and SHAAFP [52]. In previous studies, amino acids such as aspartic acids and asparagine acids in humic substances were also reported to yield N-DBPs (i.e., dichloroacetonitrile and halonitromethanes) during chlorination [16, 53]. In the present study, there was no relationship between the SHAAFP and HAA species and the humic/fulvic acid characteristics. In summary, the low-MW and low-N/C components of aromatic C and alkyl C in the humic acids might form STHMFP via incorporation reactions. In comparison, only low-N/C aliphatic compounds such as alkyl generated STHMFP in the fulvic acids. This detail will be helpful in elucidating the formation of aromatic/aliphatic N-DBPs for humic and fulvic acids in future. MWw and Δ%THLF were the appropriate factors for predicting the values of STHMFP in humic acids. In comparison, Δ%FLF and Alkyl C were applied for fulvic acids.
In particular, this study presented a new interpretation of differences between humic/fulvic acids in terms of molecular structure characteristics and chlorine reaction, including oxidation and incorporation reaction, and successfully provided sufficient factors to predict THM generation.
5. Phenanthrene adsorption isotherm of humic and fulvic acids
5.1 Comparison of phenanthrene adsorption isotherm for humic and fulvic acids
All the experimental data of humic and fulvic acids fit well with the Freundlich model, presented by the high degree of correlations (R2 > 0.99). Also, for both humic and fulvic acids, all the sorption isotherms were non-linear (n < 1). For humic acids, PHE adsorption coefficient (KOC) ranged from 3.7 × 104 to 7.0 × 104 mL/g, while for fulvic acids, it ranged from 1.4 × 104 to 2.2 × 104 mL/g. The higher sorption affinity of humic acids might be explained by the higher aromatic and condense humic structure. High isotherm linearity (n) values were associated with a low degree of natural organic matter maturation and less heterogeneous sorption-site energy distribution [54]. The n values of humic acids were lower than those of fulvic acids. In humic acids, the hydrophobic nature enhanced more coiled or aggregated structures to provide specific non-ideal binding sites [55]. In addition, high polarity might reduce the sorption affinity [56]. The Gibbs free energy values were negative for both humic and fulvic acids at 20°C, revealing the thermodynamically favorable and spontaneous adsorption process [57].
5.2 Correlations between humic and fulvic acid structural characteristics and phenanthrene adsorption isotherm
For humic acids, sorption coefficient (logKOC) was positively related to HIX and negatively correlated with S350–400. In comparison, logKOC of fulvic acids presented positive correlation with %THLF and negative relationship with %FLF and E4/E6. No relationship was observed between logKOC and humic/fulvic acid relative carbon distribution. High sorption affinity was found to be positively related to the condense structure with high aromatic C and humification, presented by the high values of HIX and %THLF and low values of %FLF and E4/E6. For humic acids, n presented positive correlation with H/C, %FLF, and %HLF and negative relationship with SUVA and %THLF. However, no relationship was observed between n values and their structural characteristics for fulvic acids. The UV spectroscopic and fluorescence characteristics of humic and fulvic acids were primarily related to their sorption ability. In a previous study, PAH sorption ability was reported to be closely related to 3D fluorescence [58].
The principal component analysis was applied for 18 selected parameters in order to interpret the specific PHE binding behavior of humic and fulvic acids. The first two principal components (PCs) explained approximately 59.32% for PC1 and 14.70% for PC2 (Figure 5). PC1 was interpreted as a factor associated with the PHE sorption behavior, whereas PC2 presented for C,H-alkyl, H/C, and free energy. Thus, logKoc was found to be positively related to MWw, SUVA, HIX, %THLF, and Mw/Mn. Moreover, n values were positively correlated with %FLF, %HLF, E4/E6, and O-alkyl (Figure 5a). From the factor score plot presented in Figure 5b, the PHE sorption behavior of humic and fulvic acids was found to be completely different. Humic acids presented higher MWw, Mw/Mn, SUVA, and %THLF, which were related to sorption affinity (logKoc). In comparison, fulvic acids were related to higher n and fulvic−/humic-like components, lower degree of condensation of the aromatic carbon, and the MW (higher E4/E6 and O-alkyl). In summary, UV and fluorescence characteristics are powerful techniques to determine the PHE sorption behavior of humic and fulvic acids extracted from soils.
Figure 5.
(a) Factor loading plot for selected structural characteristics and PHE sorption of humic and fulvic acids as the first two principal components; and (b) factor score plot for humic and fulvic acids as the first two principal components.
6. Conclusion
In this chapter, the structure characteristics of humic and fulvic acids extracted from soils were clarified. Soil humic acids were found to be more condensed and have a polymerized humic-like structure (higher SUVA, aromatic C, MWw, and %THLF). In comparison, fulvic acids contained high levels of carbonyl and quinone, more aliphatic groups, and more oxygen functional groups related to fulvic- and humic-like fluorescence materials (higher (N + O)/C, E4/E6, S350–400, %FLF, %HLF O-alkyl, and carboxyl groups). The differences in the molecular characteristics between humic and fulvic acids resulted in the differences in the generation of DBPs under chlorination and the PHE sorption behavior. For chlorination, the low-MW and low-N/C components of aromatic C and alkyl C in the humic acids might form STHMFP via incorporation reactions. However, only low-N/C aliphatic compounds such as alkyl generated STHMFP in the fulvic acids. Humic acids presented higher sorption coefficient (logKoc) because of the presence of higher MWw, Mw/Mn, SUVA, and %THLF. In comparison, fulvic acids are related to higher n and fulvic−/humic-like components and lower degree of condensation of the aromatic carbon and the MW. UV and fluorescence characteristics are powerful techniques to indicate the PHE sorption behavior of humic and fulvic acids extracted from soils. Compared with atomic ratio and relative C distribution, the UV and fluorescence characteristics approach provides the key information for water system managers to better predict and mitigate the formation of DBPs in chlorine-treated water and the behavior of hydrophobic organic contaminants in aquatic environment.
Acknowledgments
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2020R1A6A1A03042742), and by the Korea Environment Industry & Technology Institute (KEITI) through the program for the management of aquatic ecosystem health, funded by the Korea Ministry of Environment (MOE) (Grant No. 2020003030005).
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"humic acids, fulvic acids, oxidation reaction, incorporation reaction, trihalomethane, sorption isotherm, hydrophobic organic contaminants",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82281.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82281.xml",downloadPdfUrl:"/chapter/pdf-download/82281",previewPdfUrl:"/chapter/pdf-preview/82281",totalDownloads:19,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 4th 2022",dateReviewed:"May 23rd 2022",datePrePublished:"June 17th 2022",datePublished:null,dateFinished:"June 17th 2022",readingETA:"0",abstract:"Humic and fulvic acids, which can be extracted from soils, are abundant in surface water because of their high discharges from runoff during torrential rainfall, storm events, and summer monsoon. Both humic and fulvic acids adversely affect water supply as they produce disinfection by-products (DBPs) during chlorination and serve as the sorbent for the binding of hydrophobic organic contaminants. In the present study, we conducted chlorination and phenanthrene sorption for humic and fulvic acids that were extracted from nine soil samples. We also analyzed and compared their characteristics by using elemental 13C NMR analysis, spectroscopy analysis, and size exclusion chromatography. Our results showed that the changes in their structural characteristic, their DBP formation, and phenanthrene sorption behavior differed critically between humic and fulvic acids. For chlorinated humic acids, high SUVA, low molecular weight, low N/C, and low O groups of aromatic C were associated with high trihalomethane (THM) formation. In comparison, low O groups of aliphatic C in fulvic acids were associated with both oxidation and incorporation in terms of THM formation. Humic acids exhibited higher sorption ability than fulvic acids due to their higher MWw, SUVA, and %THLF. These findings provide key information for monitoring water quality in rivers and lakes.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82281",risUrl:"/chapter/ris/82281",signatures:"Hang Vo-Minh Nguyen, Jin Hur and Hyun-Sang Shin",book:{id:"11615",type:"book",title:"Humus and Humic Substances - Recent Advances",subtitle:null,fullTitle:"Humus and Humic Substances - Recent Advances",slug:null,publishedDate:null,bookSignature:"Prof. Abdelhadi Makan",coverURL:"https://cdn.intechopen.com/books/images_new/11615.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-213-1",printIsbn:"978-1-80356-212-4",pdfIsbn:"978-1-80356-214-8",isAvailableForWebshopOrdering:!0,editors:[{id:"247727",title:"Prof.",name:"Abdelhadi",middleName:null,surname:"Makan",slug:"abdelhadi-makan",fullName:"Abdelhadi Makan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Study sites and characterization methods",level:"1"},{id:"sec_2_2",title:"2.1 Soil sampling sites and extraction method",level:"2"},{id:"sec_3_2",title:"2.2 Characterization methods",level:"2"},{id:"sec_4_2",title:"2.3 Chlorination of humic and fulvic acids and THMs/HAAs measurement",level:"2"},{id:"sec_5_2",title:"2.4 PHE adsorption experiment and analysis",level:"2"},{id:"sec_7",title:"3. Characteristics of humic and fulvic acids",level:"1"},{id:"sec_8",title:"4. DPBs formation and the structural changes of humic and fulvic acids extracted from soils",level:"1"},{id:"sec_8_2",title:"4.1 DBPs formation of humic and fulvic acids extracted from soils",level:"2"},{id:"sec_9_2",title:"4.2 Changes in spectroscopic characteristics and TOC and their relationship after chlorination",level:"2"},{id:"sec_10_2",title:"4.3 Specific relationship between DPBs and humic and fulvic acid characteristics",level:"2"},{id:"sec_12",title:"5. Phenanthrene adsorption isotherm of humic and fulvic acids",level:"1"},{id:"sec_12_2",title:"5.1 Comparison of phenanthrene adsorption isotherm for humic and fulvic acids",level:"2"},{id:"sec_13_2",title:"5.2 Correlations between humic and fulvic acid structural characteristics and phenanthrene adsorption isotherm",level:"2"},{id:"sec_15",title:"6. Conclusion",level:"1"},{id:"sec_16",title:"Acknowledgments",level:"1"},{id:"sec_19",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Machado W, Franchini JC, de Fátima GM, Filho JT. Spectroscopic characterization of humic and fulvic acids in soil aggregates, Brazil. Heliyon. 2020;6:e04078'},{id:"B2",body:'Schaeffer A, Nannipieri P, Kästner M, Schmidt B, Botterweck J. From humic substances to soil organic matter–microbial contributions. In honour of Konrad Haider and James P. Martin for their outstanding research contribution to soil science. 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Environmental Science and Pollution Research. 2020;27:5337-5352'},{id:"B37",body:'Swift RS. Organic Matter Characterization. Methods of Soil Analysis. 1996:1011-1069. (SSSA Book Series)'},{id:"B38",body:'ISO 12782-4:2012. Soil quality—Parameters for geochemical modelling of leaching and speciation of constituents in soils and materials—Part 4: Extraction of humic substances from solid samples. 2017. Available from: https://www.iso.org/standard/51700.html'},{id:"B39",body:'Esfandiar N, Suri R, McKenzie ER. Simultaneous removal of multiple polycyclic aromatic hydrocarbons (PAHs) from urban stormwater using low-cost agricultural/industrial byproducts as sorbents. Chemosphere. 2021;274:129812'},{id:"B40",body:'Liu Y. Is the free energy change of adsorption correctly calculated? Journal of Chemical & Engineering Data. 2009;54:1981-1985'},{id:"B41",body:'Milonjić SK. A consideration of the correct calculation of thermodynamic parameters of adsorption. Journal of the Serbian Chemical Society. 2007;72:1363-1367'},{id:"B42",body:'Liu P, Zhou W, Cui H, Tan J, Cao S. Structural characteristics of humic substances in buried ancient paddy soils as revealed by 13C NMR spectroscopy. Environmental Geochemistry and Health. 2019;41:2459-2472'},{id:"B43",body:'Linhares CR, Lemke J, Auccaise R, Duó DA, Ziolli RL, Kwapinski W, et al. Reproducing the organic matter model of anthropogenic dark earth of Amazonia and testing the ecotoxicity of functionalized charcoal compounds. Pesquisa Agropecuária Brasileira. 2012;47(5):693-698'},{id:"B44",body:'Gauthier TD, Seitz WR, Grant CL. Effects of structural and compositional variations of dissolved humic materials on pyrene Koc values. Environmental Science & Technology. 1987;21:243-248'},{id:"B45",body:'Chen Y, Senesi N, Schnitzer M. Information provided on humic substances by E4/E6 ratios. Soil Science Society of America Journal. 1977;41:352-358'},{id:"B46",body:'Trubetskaya OE, Trubetskoj OA, Voyard G, Richard C. Determination of hydrophobicity and optical properties of soil humic acids isolated by different methods. Journal of Geochemical Exploration. 2013;132:84-89'},{id:"B47",body:'Bond T, Goslan EH, Parsons SA, Jefferson B. Disinfection by-product formation of natural organic matter surrogates and treatment by coagulation, MIEX and nanofiltration. Water Research. 2010;44:1645-1653'},{id:"B48",body:'Bond T, Henriet O, Goslan EH, Parsons SA, Jefferson B. Disinfection byproduct formation and fractionation behavior of natural organic matter surrogates. Environmental Science & Technology. 2009;43:5982-5989'},{id:"B49",body:'Johnson JD, Jensen JN. THM and TOX formation: Routes, rates, and precursors. Journal AWWA. 1986;78:156-162'},{id:"B50",body:'Gallard H, von Gunten U. Chlorination of phenols: Kinetics and formation of chloroform. Environmental Science & Technology. 2002;36:884-890'},{id:"B51",body:'Hua G, Reckhow DA, Abusallout I. Correlation between SUVA and DBP formation during chlorination and chloramination of NOM fractions from different sources. Chemosphere. 2015;130:82-89'},{id:"B52",body:'Hua L-C, Chao S-J, Huang K, Huang C. Characteristics of low and high SUVA precursors: Relationships among molecular weight, fluorescence, and chemical composition with DBP formation. Science of the Total Environment. 2020;727:138638'},{id:"B53",body:'Wu QY, Yan YJ, Lu Y, Du Y, Liang ZF, Hu HY. Identification of important precursors and theoretical toxicity evaluation of byproducts driving cytotoxicity and genotoxicity in chlorination. Frontiers of Environmental Science & Engineering. 2020;14:25'},{id:"B54",body:'Zhang J, He M, Lin C, Shi Y. Phenanthrene sorption to humic acids, humin, and black carbon in sediments from typical water systems in China. Environmental Monitoring and Assessment. 2010;166:445-459'},{id:"B55",body:'Pan B, Xing B, Tao S, Liu W, Lin X, Xiao Y, et al. Effect of physical forms of soil organic matter on phenanthrene sorption. Chemosphere. 2007;68:1262-1269'},{id:"B56",body:'Jin J, Sun K, Yang Y, Wang Z, Han L, Wang X, et al. Comparison between soil- and biochar-derived humic acids: Composition, conformation, and phenanthrene sorption. Environmental Science & Technology. 2018;52:1880-1888'},{id:"B57",body:'Jing Y, Cao Y, Yang Q, Wang X. Removal of cd(II) from aqueous solution by clay-biochar composite prepared from Alternanthera philoxeroides and bentonite. BioResources. 2020;15:598-615'},{id:"B58",body:'Lu R, Sheng GP, Liang Y, Li WH, Tong ZH, Chen W, et al. Characterizing the interactions between polycyclic aromatic hydrocarbons and fulvic acids in water. Environmental Science and Pollution Research. 2013;20:2220-2225'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Hang Vo-Minh Nguyen",address:null,affiliation:'
Department of Environment Energy Engineering, Seoul National University of Science and Technology, South Korea
Department of Environment Energy Engineering, Seoul National University of Science and Technology, South Korea
'}],corrections:null},book:{id:"11615",type:"book",title:"Humus and Humic Substances - Recent Advances",subtitle:null,fullTitle:"Humus and Humic Substances - Recent Advances",slug:null,publishedDate:null,bookSignature:"Prof. Abdelhadi Makan",coverURL:"https://cdn.intechopen.com/books/images_new/11615.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-213-1",printIsbn:"978-1-80356-212-4",pdfIsbn:"978-1-80356-214-8",isAvailableForWebshopOrdering:!0,editors:[{id:"247727",title:"Prof.",name:"Abdelhadi",middleName:null,surname:"Makan",slug:"abdelhadi-makan",fullName:"Abdelhadi Makan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"106435",title:"Dr.",name:"Jianyong",middleName:null,surname:"Zhang",email:"j.zhang@Tees.ac.uk",fullName:"Jianyong Zhang",slug:"jianyong-zhang",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Teesside University",institutionURL:null,country:{name:"United Kingdom"}}},booksEdited:[],chaptersAuthored:[{id:"31995",title:"Air-Solids Flow Measurement Using Electrostatic Techniques",slug:"air-solids-flow-measurement-using-electrostatic-techniques",abstract:null,signatures:"Jianyong Zhang",authors:[{id:"106435",title:"Dr.",name:"Jianyong",surname:"Zhang",fullName:"Jianyong Zhang",slug:"jianyong-zhang",email:"j.zhang@Tees.ac.uk"}],book:{id:"1649",title:"Electrostatics",slug:"electrostatics",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"5887",title:"Dr.",name:"Hüseyin",surname:"Canbolat",slug:"huseyin-canbolat",fullName:"Hüseyin Canbolat",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/5887/images/2416_n.jpg",biography:"Dr. Hüseyin Canbolat was born in Adana in 1966. He received his BS and MS degrees from the Middle East Technical University, Ankara, Turkey, in 1989 and 1993, respectively, and PhD degree in Electrical Engineering from Clemson University, Clemson, SC, USA, in 1997. After his PhD studies, he joined the Department of Electrical and Electronic Engineering at Mersin University in 1998. In 2012, he joined the Department of Electrical and Electronic Engineering, Ankara Yildirim Beyazit University, Ankara, Turkey. His research interests include control systems with applications to robotic and mechatronic systems, MEMS, energy systems, signal processing, measurement, and instrumentation. He is a senior member of IEEE.",institutionString:null,institution:{name:"Ankara Yıldırım Beyazıt University",institutionURL:null,country:{name:"Turkey"}}},{id:"107427",title:"Dr.",name:"Toshko",surname:"Boev",slug:"toshko-boev",fullName:"Toshko Boev",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sofia University",institutionURL:null,country:{name:"Bulgaria"}}},{id:"108883",title:"Dr.",name:"A",surname:"Cherstvy",slug:"a-cherstvy",fullName:"A Cherstvy",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Forschungszentrum Jülich",institutionURL:null,country:{name:"Germany"}}},{id:"109382",title:"Mr.",name:"Julien",surname:"Dura",slug:"julien-dura",fullName:"Julien Dura",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Institut des Matériaux, de Microélectronique et des Nanosciences de Provence",institutionURL:null,country:{name:"France"}}},{id:"114625",title:"Prof.",name:"Vikas",surname:"Nanda",slug:"vikas-nanda",fullName:"Vikas Nanda",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Robert Wood Johnson University Hospital",institutionURL:null,country:{name:"United States of America"}}},{id:"114631",title:"Prof.",name:"I. John",surname:"Khan",slug:"i.-john-khan",fullName:"I. John Khan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Robert Wood Johnson University Hospital",institutionURL:null,country:{name:"United States of America"}}}]},generic:{page:{slug:"cro-terms-and-conditions",title:"Odredbe i uvjeti",intro:'
Ove Odredbe i uvjeti ističu pravila i regulacije u svezi korištenja IntechOpenove stranice www.intechopen.com i svih poddomena u vlasništvu IntechOpena, tvrtke sa sjedištem u 5 Princes Gate Court, London, SW7 2QJ, Ujedinjeno Kraljevstvo.
',metaTitle:"Odredbe i uvjeti",metaDescription:"Ove Odredbe i uvjeti ističu pravila i regulacije u svezi korištenja IntechOpenove stranice www.intechopen.com i svih poddomena u vlasništvu IntechOpena, tvrtke sa sjedištem u 5 Princes Gate Court, London, SW7 2QJ, Ujedinjeno Kraljevstvo.",metaKeywords:null,canonicalURL:"/page/cro-terms-and-conditions",contentRaw:'[{"type":"htmlEditorComponent","content":"
1. Odredbe
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Pristupom na stranicu www.intechopen.com slažete se s ovim odredbama, sa svim primjenjivim zakonskim odredbama, te se slažete s poštovanjem svih lokalnih zakona. Korištenje i/ili pristup ovoj stranici temelji se na potpunom prihvaćanju ovih odredbi. Svi materijali na ovoj stranici zaštićeni su primjenjivim zakonima o autorskim pravima i žigu.
\\n\\n
Sljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\\n\\n
Klijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\\n\\n
Kompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\\n\\n
Stranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\\n\\n
Sve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
\\n\\n
2. Licenca
\\n\\n
Osim ako nije suprotno navedeno, IntechOpen i/ili svi davatelji licence vlasnici su intelektualnog vlasništva nad svim materijalima na www.intechopen.com. Sva prava intelektualnog vlasništva su pridržana. Stranice sa www.intechopen.com možete gledati, preuzimati, dijeliti, dijeliti poveznice i printati za osobnu uporabu, a temeljem pravila sadržanih u ovim Odredbama i uvjetima.
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3. Kolačići
\\n\\n
Mi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\\n\\n
4. Ograničenja odgovornosti
\\n\\n
IntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\\n\\n
5. Točnost materijala
\\n\\n
Materijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
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6. Poveznice
\\n\\n
IntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\\n\\n
Zadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\\n\\n
Ako smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\\n\\n
7. Okviri (Frames)
\\n\\n
Bez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\\n\\n
8. Promjene
\\n\\n
IntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\\n\\n
9. Nadležno pravo
\\n\\n
Ove Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
Pristupom na stranicu www.intechopen.com slažete se s ovim odredbama, sa svim primjenjivim zakonskim odredbama, te se slažete s poštovanjem svih lokalnih zakona. Korištenje i/ili pristup ovoj stranici temelji se na potpunom prihvaćanju ovih odredbi. Svi materijali na ovoj stranici zaštićeni su primjenjivim zakonima o autorskim pravima i žigu.
\n\n
Sljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\n\n
Klijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\n\n
Kompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\n\n
Stranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\n\n
Sve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
\n\n
2. Licenca
\n\n
Osim ako nije suprotno navedeno, IntechOpen i/ili svi davatelji licence vlasnici su intelektualnog vlasništva nad svim materijalima na www.intechopen.com. Sva prava intelektualnog vlasništva su pridržana. Stranice sa www.intechopen.com možete gledati, preuzimati, dijeliti, dijeliti poveznice i printati za osobnu uporabu, a temeljem pravila sadržanih u ovim Odredbama i uvjetima.
\n\n
3. Kolačići
\n\n
Mi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\n\n
4. Ograničenja odgovornosti
\n\n
IntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\n\n
5. Točnost materijala
\n\n
Materijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\n\n
6. Poveznice
\n\n
IntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\n\n
Zadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\n\n
Ako smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\n\n
7. Okviri (Frames)
\n\n
Bez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\n\n
8. Promjene
\n\n
IntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\n\n
9. Nadležno pravo
\n\n
Ove Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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This new technology can be used in various wireless power transfer applications with different specifications, necessities, and restrictions such as in electric vehicles and consumer electronics. A typical ICWPT system involves a loosely coupled magnetic coupling structure and power electronics circuitries as an integrated system. In this chapter, the emphasis is placed on the magnetic coupling structure, which is the most important part of the system. Although this technology has motivated considerable research and development in the past two decades, still there are several theoretical studies such as the level of the operating frequency, operating at high secondary circuit quality factor, coupling efficiency, etc., that need further investigation to fully develop the governing mathematical relationships of this technology.",book:{id:"5187",slug:"wireless-power-transfer-fundamentals-and-technologies",title:"Wireless Power Transfer",fullTitle:"Wireless Power Transfer - Fundamentals and Technologies"},signatures:"Ali Abdolkhani",authors:[{id:"179618",title:"Dr.",name:"Ali",middleName:null,surname:"Abdolkhani",slug:"ali-abdolkhani",fullName:"Ali Abdolkhani"}]},{id:"78626",title:"Electricity Storage in Local Energy Systems",slug:"electricity-storage-in-local-energy-systems",totalDownloads:221,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Traditionally, power system operation has relied on supply side flexibility from large fossil-based generation plants to managed swings in supply and/or demand. An increase in variable renewable generation has increased curtailment of renewable electricity and variations in electricity prices. Consumers can take advantage of volatile electricity prices and reduce their bills using electricity storage. With reduced fossil-based power generation, traditional methods for balancing supply and demand must change. Electricity storage offers an alternative to fossil-based flexibility, with an increase expected to support high levels of renewable generation. Electrochemical storage is a promising technology for local energy systems. In particular, lithium-ion batteries due to their high energy density and high efficiency. However, despite their 89% decrease in capital cost over the last 10 years, lithium-ion batteries are still relatively expensive. Local energy systems with battery storage can use their battery for different purposes such as maximising their self-consumption, minimising their operating cost through energy arbitrage which is storing energy when the electricity price is low and releasing the energy when the price increases, and increasing their revenue by providing flexibility services to the utility grid. Power rating and energy capacity are vitally important in the design of an electricity storage system. A case study is given for the purpose of providing a repeatable methodology for optimally sizing of a battery storage system for a local energy system. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. 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He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:6,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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Ribeiro-Barros",profilePictureURL:"https://mts.intechopen.com/storage/users/171036/images/system/171036.jpg",institutionString:"University of Lisbon",institution:{name:"University of Lisbon",institutionURL:null,country:{name:"Portugal"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Sustainable Economy and Fair Society",value:91,count:1}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:1}],authors:{paginationCount:250,paginationItems:[{id:"274452",title:"Dr.",name:"Yousif",middleName:"Mohamed",surname:"Abdallah",slug:"yousif-abdallah",fullName:"Yousif Abdallah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",biography:"I certainly enjoyed my experience in Radiotherapy and Nuclear Medicine, particularly it has been in different institutions and hospitals with different Medical Cultures and allocated resources. Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"243698",title:"Dr.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"312999",title:"Dr.",name:"Bernard O.",middleName:null,surname:"Asimeng",slug:"bernard-o.-asimeng",fullName:"Bernard O. 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\r\n\tSustainable approaches to health and wellbeing in our COVID 19 recovery needs to focus on ecological approaches that prioritize our relationships with each other, and include engagement with nature, the arts and our heritage. This will ensure that we discover ways to live in our world that allows us and other beings to flourish. We can no longer rely on medicalized approaches to health that wait for people to become ill before attempting to treat them. We need to live in harmony with nature and rediscover the beauty and balance in our everyday lives and surroundings, which contribute to our well-being and that of all other creatures on the planet. This topic will provide insights and knowledge into how to achieve this change in health care that is based on ecologically sustainable practices.
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