Description of various roles of non-structural proteins from
\r\n\tEqually important are the consequences deriving from the extraordinary nature of the present times. The COVID-19 pandemic and the restrictive measures to contain the infection (lockdown and "physical distancing" in primis) have revolutionized the lives, and a distortion/modification of habits, rhythms, arrangements will continue to be necessary.
\r\n\tGovernments have implemented a series of actions to mitigate the spread of infections and alleviate the consequent pressure on the hospital system. On the other hand, the Covid-19 pandemic has caused a series of other cascading effects that will probably be much more difficult to mitigate and which expose to complex consequences. The past two years have brought many challenges, particularly for healthcare professionals, students, family members of COVID-19 patients, people with mental disorders, the frail, the elderly, and more generally those in disadvantaged socio-economic conditions, and workers whose livelihoods have been threatened. Indeed, the substantial economic impact of the pandemic may hinder progress towards economic growth as well as progress towards social inclusion and mental well-being.
\r\n\t
\r\n\tAlthough in all countries the knowledge on the impact of the pandemic on mental health is still limited and mostly derived from experiences only partially comparable to the current epidemic, such as those referring to the SARS or Ebola epidemics, it is likely that the demand for intervention it will increase significantly in the coming months and years. The extraordinary growth of scientific research in the field of neuroscience now offers the possibility of a new perspective on the relationship between mind and brain and generates new scenarios in understanding the long wave of the pandemic and in the prospects for treatment. Moreover, the pandemic also has led to opportunities to implement remote monitoring and management interventions.
\r\n\t
\r\n\tOverall this volume will address the complex relationship existing between COVID-19, mental health, acquired knowledge, and possible interventions taking a highly multidisciplinary approach; from physiological and psychobiological mechanisms, and neuromodulation through medical treatment, psychosocial interventions, and self-management.
In a communication situation, participants exchange signs and coordinate their overall behavior accordingly. It is, therefore, obvious that there is something that these signs confer, their meaning. But where exactly is this meaning to be found?
The rationalistic approach views meaning-making as a mental process that is based on the link between the individual minds of the participants and a sort of universal mind that contains eternal abstract meanings that correspond to the logical structure of linguistic expressions. This forms the basis of denotational semantics and finds its most developed form in possible world semantics.
On the contrary, embodied approaches regarding meaning are material sedimentation in the neural systems of the communication partners that results from accumulated interactions with a common environment. From this point of view, meanings emerge as orientation fixpoints in structurally coupled cognitive systems and are not pre-existing ideal entities. This offers a semiotic background for neuroscience research that aims to identify the neural signatures of cognitive processes.
Dialogism puts in the center of attention the mutual influence loop in communication acts. With this focus, meaning-making is neither the result of a mental link between individual and universal mind nor of individual activations of material neural sedimentations, but of an existential tension field between communication participants.
For the dualistic paradigm, communication is based on the exchange of meaning-entities between the minds of the speaker and the listener. To refer to an object the speaker verbalizes his/her actual perception of it with a linguistic expression that carries an invisible label, enabling the listener to refer to the same object. But speaker and listener have different mental images of the same world fragment, the same object viewed from two perspectives generates different perceptions. How is it possible that the speaker transfers to the listener his/her mental image? Because there is an intermediary objective meaning-entity, the transfer of which enables the speaker and the listener to refer to the same object, although the individual perceptions of it might be different.
While Frege uses “Bedeutung” for reference and “Sinn” for meaning, Husserl in the Logical Investigations [2] uses “Bedeutung” (meaning) for the Fregean “Sinn” ([2], p. 58). The objective meaning-entity lies for Husserl in the logical content of a phenomenological act. Whereas the real content stands for the unrepeatable actual experience, the logical content captures—like a mental telescope—the abstract scheme that corresponds to the underlying propositional structure. The logical content of linguistic expressions is an instantiation of pre-existent, identical, eternal, shareable meaning-entities.
These meanings exist “before” they are instantiated in linguistic expressions in a third domain beyond res cogitans and res extensa, they cannot be grasped with the senses, but at the same time, they do not need to be carried by consciousness, as described by Frege for the thoughts associated with meanings.
These eternal meanings might not exist in God’s mind ([2], p. 106), but are, nevertheless, universal truths that wait to be discovered like undiscovered planets ([3], p. 44). Of course, the prototype of these meanings is the mathematical truths; they are evident for all minds and can be accessed through abstraction. These inhabitants of the meaning domain are “allgemeine Gegenstände” (universal objects) that are instantiated in actual expressions.
Communication is, therefore, possible, because a meaning-entity, which actualizes an instantiation of an eternal, universal, in the third domain pre-existing ideal meaning, is exchanged between the participants, enabling them to refer to the same objects and facts in the world.
However, in contrast to mathematical truths, linguistic expressions are context-sensitive; the meaning of indexicals can only be determined according to an actual situation. Meaning is anchored in the horizon of the phenomenological act, leading to a possible world semantics point of view, where meaning becomes a function from a possible to world to the corresponding extension.
The phenomenological horizon (
The meaning of an expression is now an invisible operation that associates with each actual or potential situation a logical content that corresponds to the extracted propositional structure. Either in the form of ideal, eternal entities or as functions from a domain of possible worlds to a codomain of logical descriptions, the dualistic approach presupposes something like a universal mind, where all these contents float. Humans can communicate because, by having individual access to this universal mind, they can identify identical transferable meanings.
Within this framework, paralinguistic signs and soliloquy do not have a meaning (as also Husserl is pointing out in the Logical Investigations), because these signs do not contribute to the logical content and during soliloquy, there is no transfer.
If the grasping of meaning presupposes propositional analysis, then infants would not be able to communicate. Humans are not born as isolated individuals that need to develop intelligent faculties before they can communicate. Instead, newborns are engaged in preverbal proto-conversations with the mother [6]. This dyadic interaction evolves through gaze following to secondary intersubjectivity that involves a mother-baby-object triadic relation [7]. Meaning exchange must be grounded, therefore, in preconceptual bodily anchored mechanisms. This has indeed been demonstrated with the discovery of mirror neurons.
For Merleau-Ponty, who first introduced the corporeal embedding in the world as the basis for meaning-making, the meaning of an object is not an ideal entity but the residuum of experiences from interacting with this object in situations; it is its
This residuum of experiences is materialized as sedimentation of interactions in brain networks. A concept is a dynamically distributed system in the brain [10]. It is an assembly of neurons that contain a recording of schematic aspects (extracted by selective attention) of a brain state associated with a perception [11]. Meaning is created as a result of the whole organism interacting with the environment. For example, activations in brain structures and sensorimotor systems are intercoupled [12]. It is also not localizable in a specific brain area, because the binding of multimodal semantic features relies on long-range connections; it is binding by synchrony [13].
Moreover, as the evolution of intersubjectivity in infants demonstrates [14], reference to objects emerges from intersubjectivity and it is not the result of the meaning exchange between isolated individuals. Humans are born into intersubjectivity.
Meaning emerges from internalization [15] of social interactions and not—as in the dualistic scenario—social interaction emerges from the meaning exchange between individuals. The alter ego stands for another point of view of the ego. Embodied interactions from cognitive systems with similar capabilities with an environment with the same affordances result in comparable sedimentations. A universal Leib penetrates ego and alters ego.
The topology of a chair is not homogenous. Regions with high density, resulting from often interactions with joint attention, form intersubjective concepts that emerge as common orientation dimensions. Concepts are not eternal entities in an ideal third domain but emerge from intersubjective communication and are materializations of recurrent patterns. Such materializations stored in long-term memory create the categorial framework that enables social interaction. But it is also possible that intermediate forms, such as
The sedimented experience from interacting with a certain sort of objects constitutes a simulator. When perceiving or imagining an object, a distributed pattern becomes activated across relevant brain areas, and it is this distributed neural network that simulates the relevant concept [18]. Understanding is based on simulation [19].
Neuroscience experiments demonstrated indeed a similarity between neural activations related to an experience and its recall. MRI-based analysis of neural activity during movie-viewing and spoken-recall identified pattern similarity in a large set of brain regions [21].
In contrast to the dualistic approach, paralinguistics signs form inseparable parts of meaning, since they are captured in combination with other multimodal elements that are sedimented in the concept simulators. Linguistic utterances can be seen as phonetic gestures that emerged in evolutionary steps from facial and manual gestures [22].
Construction linguistics views syntactic structures not as a result of
Communication is more than individual activation of the brain networks of the participants; it creates a dynamic seamless integration of speaker and listener. Any utterance in a dialog is completed, when it is “understood” by the listener; every comprehension entails a responsiveness attitude, and every utterance anticipates an answer [25].
A tension field penetrates communication partners. There is a continuous loop between the expectations as well as between the expectations about the expectations of the speaker and the listener that shapes communication and enables the prediction of classes of behavior [26]. The neural signature of this loop lies in the interaction between posterior and anterior brain networks [27].
Neural activations in speaker and listener are correlated. To capture this interdependency, the method of hyperscanning has been developed, whereby neural activations of communicating partners are recorded simultaneously. Hyperscanning experiments provide evidence that behavioral synchrony and turn-taking are accompanied by brain oscillatory couplings [28].
Meaning is, therefore, not only determined by the activation of the speaker’s and listener’s simulators, but also created and modified through the situative coupling in a communication act. Verbal exchange and the resulting synchronized activations in the brains of speaker and listener cannot be reconstructed by integrating the isolated individual activations [29]. There is something more: mutual tuning creates a field that co-determines meaning. This includes both the loop of expectations and extralinguistic dimensions of the experienced situation.
Volosinov [30] gives an example of two men, who are in a context of a lingering winter and the start of a new snowfall. One of them says “well” and the other does not answer. The meaning of “well” in this dialog, followed by the silence, is an “agreement” between the participants that they were tired from a long winter and disappointed that the spring did not start. Silence is not devoid of meaning; it is a dialogic move.
The carrier of meaning is the voice.
The voice is also bodily anchored and resonates in the listener, leading to activations in core mentalizing brain areas showing strong functional connectivity with mirror neurons [33].
In every dialog, there are not only the voices of the communicating partners but also the silent voices from others not present, such as
These imagined interactions form an inner dialog with others, leading to a dialogic self [35]. In contrast to the rationalistic self, the dialogic self can change its stance among different and even antagonizing voices creating dialogical relations between them. Meaning always includes an intrinsic relation to autonoesis [36], meaning is meaning for me. Soliloquy plays—in contrast to the dualistic approach—a significant role in the formation of meaning. Linguistic social exchanges are transformed into an internalized “conversation” with the self [37]. Both inner and overt speeches activate similar brain areas. Neuroscience experiments have shown that inner speech is a simulation of speech, including motor planning, but excluding motor execution [38]. The dialogic self creates an autobiographic narrative in dialog with significant others [39].
Meaning is constituted and reconstituted from the resonance of the outer and inner voices of the participants and includes a verbal, a situational and an autonoetic dimension. In the example from Volosinov above, the meaning of “well” in the specific communication act emerges as a Gestalt that balances the mutually experienced situation with the autonoetic voices of the two men; it exists, as such, in
Recurrent dialogic encounters with significant others result to an entanglement of the voices of the participants, unfolding a dialogic space of open-ended dialogs that codetermine meaning and the autobiographic narrative. The participating voices complement each other, and this complementarity creates meaning from situated experiences for the dialogic self.
Autonoetic exploration of the dialogic space determines and redetermines a
In every utterance several voices are floating, some are speaking for one positioning, some for another and some for two or more positioning. Voices are fluctuating along two dimensions: (1) overlapping or conflicting semiotic positions and (2) intensity. The degree of conformity or conflict to a certain semiotic position creates a conformity horizon, while the intensity of a voice an intensity horizon. Dominant voices possess a high degree of conformity and intensity and suppress less audible voices but suppressed voices can come back and challenge dominant ones.
Meaning from a dialogic point of view emerges as a Gestalt that balances the positioning with the positioning of the other through interbrain synchronization, leading to an interpretation of the experienced situation and to an evolution of the dialogic space, which in turn leads to an evolution of positioning. Meaning is, therefore, both actively co-constituted during communication and always intrinsically coupled to existential positioning of the dialogic self. Starting from an analysis of communication acts as experienced, dialogism dives deeper into the meaning-making phenomenon.
For the dualistic paradigm, meaning exists in an eternal mental domain, to which communication participants have access through their individual minds. For the embodied approach, meaning is a dynamic system in the matter of an individual brain network. For the dialogic approach, meaning emerges from interbrain synchronization. The place of meaning is neither in mental domain nor solely in neural materialization, but rather in the mutual influence between two or more cognitive systems creating an existential space of ongoing dialogs between voices that carry a positioning perspective.
This space can be viewed as an assemblage of voices in the sense of Deleuze and Guattari [42]. Assemblages bind together parts in a way that the whole creates emergent properties, while the parts sustain their autonomy; they are not fused in a single structure and can participate in the formation of other assemblages. Assemblages are also “concrete”, their parts are not placeholders for others with the same structural value, they
The topology of assemblages is nomadic, and it is created dynamically in a self-organizing manner and captures the current positioning. Assemblages oscillate between territorialization and deterritorialization. Positioning-compatible experiences reinforce the intensity of the voice of the current positioning, while non-compatible ones question it and trigger a perturbation that can lead to a new positioning.
The challenge and the opportunity of cognitive semiotics is to describe the genealogy of meaning from the complexification of these semiotic processes.
The Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (
Generally, time is a vital factor in the pandemic condition, so that, rapid detection, vaccination, and treatment methods can significantly reduce mortality. De novo drug discovery and development for lesser-known diseases such as COVID-19 is costly and tedious. Consequently, alternative methods such as the computational drug repurposing approach can accelerate the discovery of new drugs. In this regard, several pipelines have been introduced for in silico drug repositioning against COVID-19. Lately, molecular docking as a popular bioinformatics method has been highly regarded as the core of the most drug repositioning process to achieve effective drug candidates to combat COVID-19 [4, 5, 6]. In this chapter, we discussed new advancements and challenges in drug repositioning by molecular docking of pharmaceutical resources to the identification of potential
Protein name | Length (amino acid) | Role | References |
---|---|---|---|
NSP1 | 180 | Host translation inhibitor and also degrade host mRNAs | [1] |
NSP2 | 638 | Binds to prohibitin 1 and prohibitin 2 | [2] |
NSP3 | 1945 | Responsible for release of NSP1, NSP2, and NSP3 | [3] |
NSP4 | 500 | Viral replication-transcription | [4] |
NSP5 | 306 | Cleaves at multiple distinct sites to yield mature | [5] |
NSP6 | 290 | Induces formation of ER-derived autophagosomes | [6] |
NSP7 | 83 | Forms complex with NSP8 and NSP12 to yield the RNA polymerase activity of NSP8 | [7] |
NSP8 | 198 | Makes heterodimer with NSP8 | [8] |
NSP9 | 198 | bind to helicase | [5] |
NSP10 | 139 | Unknown | [5] |
NSP11 | 13 | Unknown | [5] |
NSP12 | 932 | Replication and methylation | [9] |
NSP13 | 932 | A helicase core domain | [10] |
NSP14 | 527 | Exoribonuclease activity a | [5] |
NSP15 | 346 | Mn(2 +)-dependent endoribonuclease activity | [5] |
NSP16 | 298 | Methyltransferase | [11] |
Description of various roles of non-structural proteins from
The role(s) of NSP10 and NSP11 is(are) still not well understood.
Generally, a probable antiviral drug target is a molecule (often a protein) with a vital role in the life cycle of the planned virus [14, 15]. Accordingly, to date, several structural and accessory proteins from
The trimeric
Generally, the virus replication directly affects the viral burden and symptom severity in viral infections. Therefore, targeting the key molecules in the
Unfortunately, to date, there is no specific anti-COVID-19 drug. However, the results of some studies suggested that other anti-viral medicines could be repurposed as effective anti-COVID-19 drugs. Remdesivir, an FDA-approved repurposed antiviral drug, is only in used approved anti-viral therapy against COVID-19 [30]. However, other anti-viral and non-antiviral drugs have also been used for studying their anti-COVID-19 activities. Hydroxychloroquine, an anti-malaria drug with polymerase inhibitory activity, was the first repurposed drug against COVID-19, which was supported by some in vitro effectiveness evidence. However, further clinical trials indicate that there is no association between hydroxychloroquine administration and reduction in the death rate due to COVID-19. Kaletra (a brand name of lopinavir/ritonavir complex) is an approved anti-human immunodeficiency virus (HIV) protease inhibitor, which empirically evaluated for 3CLpro inhibitory activities. Despite, promising in vitro results, clinical trials have not confirmed the significant efficacy of Kaletra in individuals hospitalized with COVID-19. Favipiravir, a purine nucleic acid analog, is another anti-viral drug that is repurposed against mild to moderate COVID-19. The results of clinical trials suggest that Favipiravir has no significant beneficial effect on the mortality rate in patients with COVID-19. Additionally, some other drugs such as colchicine, oseltamivir, ivermectin, tocilizumab, nafamostat, camostat, famotidine, umifenovir nitazoxanide are under evaluation for investigating their probable anti-COVID-19 activities [31, 32, 33].
Because of the costly, time-consuming, and complexity of De novo drug discovery, until now all proposed anti-COVID-19 drug candidates are repurposed drugs. Drug repurposing also known as drug re-tasking is a procedure of recognizing new therapeutic application(s) for previously approved, failed, investigational, and or already marketed drugs. Naturally, the drug-repurposing process is based on two fundamental principles including interdependence between different diseases and the confounding nature of drugs. Therefore, drug-repositioning approaches could be categorized into drug-based and disease-based strategies.
The drug-based strategies are vastly based on drug-related data and are used for better understanding the role of pharmacological properties and defining the possibility of defining new pharmaceutical capabilities. Despite the advantages of experimental drug repositioning, the fact that it was time consuming still remained as the main limitation for drug discovery, especially in a pandemic condition. Furthermore, conventional methods use small datasets and biological networks, which may lead to unreliable discoveries.
Nowadays, different computational methods have been introduced that can accelerate the drug-repositioning process [27]. In the next sections, the most common computational approaches for drug repositioning are propounded.
In a drug discovery project, target identification and validation are key steps that directly affect drug efficacy, as well as probable side effect(s). Theoretically, a drug target molecule can be selected among a wide range of biological entities including proteins, genes, and RNAs. However, an ideal drug target molecule should be drug accessible, efficacious, safe, and meet clinical and commercial requirements [4]. Target identification can be performed by different tools such as analysis of gene modifications, protein overexpression, signaling pathways, protein interactions, and recent bioinformatics evaluations. Regarding antiviral drug discovery, different targets such as envelop proteins, S-adenosyl-L-homocysteine hydrolase, orotidine 5′-phosphate decarboxylase, cytidine triphosphate synthetase, inosine monophosphate dehydrogenase, and DNA/RNA polymerase have been investigated for discovering effective antiviral drugs [34, 35, 36, 37]. The identified target molecules can be validated by knocking in/down/out the genes, monoclonal antibodies, and chemical genomics [4, 38]. As mentioned, recently bioinformatics methods, such as ligand-based interaction fingerprint (LIFt), protein-ligand interaction fingerprints (PLIF), and network-based drug discovery, have successfully been used for drug target identification [39].
There are now a large number of diseases- and drugs-linked information such as gene sequences, protein–protein interactions, and drug–protein interactions with increasing rapid growth, which needs effective approaches to quick access and analysis of hidden information. Commonly, text mining is the most applicable method in the majority of data mining–related studies. In the field of computational drug repurposing, text mining has been used to find the gene, drug, and diseases-related data and then categorize the relevant entities. Regarding drug repurposing, text mining has successfully been used in several studies [40, 41]. Brown et al. suggested an online text-mining server with the ability to drug clustering based on the similarity of their physicochemical properties [42]. A text mining-based tool was also introduced by Leaman et al. for identifying disease-related information mentioned in the literature [43]. In another study, Papanikolaou et al. used text mining to recognize biological entities in the Drug Bank database. The retrieved data were then clustered by different algorithms and used for obtaining novel drug–drug relations [44].
Machine learning, a crucial subset of artificial intelligence (AI), has been combined into many fields, such as data generation and analytics. Related to drug discovery, ML algorithms may participate in target and lead discovery as well as develop quantitative structure–activity relationships. Briefly, in machine learning-based drug repositioning, different algorithms, such as artificial neural networks (ANNs), support vector machines (SVMs), and random forest (RF), were trained by numerical forms of different features of drugs, diseases, genes, and so on. The trained algorithms can then predict the drug ability of unknown compounds [45]. In this regard, Gottlieb et al. used drug–drug and disease–disease similarity events as grouping features for training a logistic regression classifier and prediction of drug-disease associations [46]. Similarly, Napolitano et al. introduced a SVM model trained by drug-related similarities with the ability to forecast the therapeutic class of United States Food and Drug Administration (FDA)-approved compounds [47]. Aliper et al. introduced a fully connected deep neural network algorithm trained by gene expression signatures for predicting therapeutic potentials and new drug suggestions [48].
Biological networks, an outstanding way of modeling biological entities and their interactions, can supply significant insight into the mechanism action of drugs and drug targets and symptoms of diseases. The models can be used to determine informative associations between genes, chemicals, proteins, phenotypes, and any other biological entities by statistical analysis, computational models, and leveraging graph theory concepts. Based on the data sources, network analysis can be classified into metabolic networks, protein–protein interaction networks, drug–drug interaction networks, drug-side effect association networks, disease–disease interaction networks, and gene regulatory networks. Consequently, bionetworks and their analysis can be used to identify potential therapeutic agents and drug repositioning [49, 50, 51].
Studying the ligand-protein interactions at the molecular level has a crucial role in pharmaceutical research. Therefore, the scientific community focused on the exploration of the binding phenomenon over the years. Accordingly, some theories, such as lock and key hypothesis, induced-fit theory, and conformational selection were introduced for the interpretation of ligand–protein interactions [52]. Historically, the refinement of a complex structure by optimization of the separation between the partners was the first description of the docking term in 1970. Molecular docking was first being developed in 1980 to predict the best matching binding mode and the molecular interactions of a ligand to a macromolecular partner through the generation of a number of probable orientations of the ligand inside the protein cavity. The method comprises two interrelated steps including orientations sampling and a scoring function, which are responsible for reproducing experimental binding mode and ranking of prepared complexes [52, 53]. Molecular docking can classify into rigid, semi-flexible, and flexible types, according to the degrees of flexibility of the ligand and receptor. In the rigid docking-like to lock-key theory, both ligand and protein are considered rigid entities and hence, there is no internal degree of freedom. Semi-flexible docking is a molecular docking simulation with flexible ligand and rigid receptors. Thus, all degrees of freedom of ligand are explored. Recently, several online and standalone software such as AutoDock, AutoDock Vina, Molegro Virtual Docker, Gold, Surflex-Dock, GLIDE, FlexX, DOCK, FRED, and so on, have been developed for computing different types of molecular docking. Most available software for molecular docking uses flexible ligands and several are trying to model flexible receptor proteins. In recent years, with promising advancements in optimization and the development of new molecular docking algorithms, numerous publications have been planned for comparing the performance of different molecular docking tools. However, it should be stressed that comparison between molecular docking methods is problematic, due to the dependance on docking performance with classes of the subjected targets. The ability of molecular docking methods to reveal the possibility of enzymatic reactions is a compelling reason for various applications related to computational drug design and repurposing, hit identification, lead optimization, binding site prediction, mechanisms of enzymatic reactions, and protein engineering [54, 55, 56]. Since the emergence of COVID-19, several molecular docking-based studies [57, 58, 59, 60, 61, 62] have been planned to introduce effective anti-COVID-19 drugs by means of drug repositioning. In Figure 1, the main steps of a molecular docking-based drug repurposing study are represented.
Schematic representation of the main steps of a molecular docking-based drug repositioning process. Target identification, ligand preparation, and results interpretation are the three main steps.
As a popular bioinformatics method, recently several types of research have been conducted to reposition approved drugs against COVID-19 by means of molecular docking. Despite similar aspects and methodology, the used software, subjected target and ligands can affect the outputs of molecular docking-based drug repositioning [54, 63]. In Table 3, some recently published works associated with molecular docking-based drug repurposing are presented. Based on our best knowledge,
Subjected target | ligands | Proposed drug or ligand | References |
---|---|---|---|
Mpro | FDA-approved drugs | binifibrate and bamifylline | [64] |
Mpro | 4384-approved drugs | Daunorubicin and eight other compounds | [65] |
Mpro | 6218-approved drugs | Emodin and blonanserin | [66] |
RBD, NSP 10, NSP 16, Mpro, and RdRp | Brazilian Public Health System-approved drugs | penciclovir, ribavirin, and zanamivir | [67] |
Mpro | Drug Bank database | levothyroxine, amobarbital and ABP-700 | [68] |
spike glycoprotein | FDA-approved drugs | Conivaptan and Trosec | [14] |
spike glycoprotein | Plant secondary metabolites | Dicaffeoylquinic acid | [15] |
Mpro | FDA-approved antiviral drugs | Lopinavir-Ritonavir, Tipranavir, and Raltegravir | [16] |
papain like protease | Plant secondary metabolites | I-Asarinin | [17] |
Mpro | superDRUG2 database | Binifibrate and Bamifylline | [18] |
Mpro | Plant secondary metabolites | ursolic acid, carvacrol and oleanolic acid | [24] |
RdRp | FDA-approved anti-viral drugs | remdesivir, ribavirin, sofosbuvir and galidesivir | [25] |
Mpro | FDA approved drugs | remdesivir and glycyrrhizin | [26] |
Mpro and RdRp | Plant secondary metabolites | cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine | [27] |
Recently published molecular docking-based drug repositioning research for introducing novel drugs against COVID-19.
The
As mentioned in Section 3.2, due to the important role in the viral life cycle alongside the absence of closely related homologs in humans, the 3CLpro is considered a proper target for discovering effective antiviral drugs against
A small molecule–protein molecular docking study is based on the prediction of probable interactions between the ligand and its receptor. Obtaining the three-dimensional structures of both the ligand and receptor is the first vital step for performing a molecular docking process. Therefore, the raw three structures of a set of FDA-approved antiviral protease inhibitors, as well as 3CLpro from
Approved drug | Chemical formula | Accession number |
---|---|---|
Darunavir | C27H37N3O7S | DB01264 |
Tipranavir | C31H33F3N2O5S | DB00932 |
Atazanavir | C38H52N6O7 | DB01072 |
Amprenavir | C25H35N3O6S | DB00701 |
Fosamprenavir | C25H36N3O9PS | DB01319 |
Nelfinavir | C32H45N3O4S | DB00220 |
Ritonavir | C37H48N6O5S2 | DB00503 |
Indinavir | C36H47N5O4 | DB00224 |
Saquinavir | C38H50N6O5 | DB01232 |
Chemical formula and drug bank accession number of nine FDA-approved antiviral protease inhibitors subjected for repurposing against
Despite primary screening done by the blind docking method, several studies have been conducted to introduce effective 3CLpro inhibitors. However, to date, binding pockets and key amino acids in the enzyme catalytic activity are not well known. Therefore, as primary screening, the blind docking processes through Molegro Virtual Docker 6.0 software were performed between the standard drugs and the 3CLpro to determine the key amino acid(s). In blind molecular docking, the whole surface of a subjected receptor is considered for evaluation of probable interactions with the ligand.
After determining the total affinities of the standard drugs to the 3CLpro as well as more reactive amino acids, targeted molecular docking studies were conducted between the receptor the three top-scoring docked ligands in a grid box, which covers the key amino acid(s) by Autodock 4.2.6 software.
The results of the primary screening are presented in Table 5. The results demonstrated that Amprenavir, Tipranavir, and Fosamprenavir had a higher binding affinity to the 3CLpro than the other tested viral protease inhibitors with Moldock scores of −160.384, −158.307, and −146.601 respectively. Furthermore, it was clear that GLN 189 is a key amino acid in the 3CLpro interactions with different proteases. Therefore, a targeted molecular docking between the three top-scoring standard protease inhibitors (Amprenavir, Tipranavir, and Fosamprenavir) were also performed in a grid box with the center of GLN189. As depicted in Figure 2, the subjected standard drugs also showed high affinity to the 3CLpro with binding energies of −5.3, −5.1, and −6.2 kcal/mol respectively. Subsequently, due to the high affinity of Fosamprenavir to the 3CLpro, this antiviral protease inhibitor could be considered for further in silico, in vitro, and in vivo evaluation to develop as a repurposed anti
Drug | Moldock score (kcal/mol) | Key amino acids |
---|---|---|
Darunavir | −110.402 | THR190,ARG188, ASN142,TYR54,GLN189,ASP187 |
Tipranavir | −158.307 | THR26,ASN142,GLN189,ARG188,GLU166 |
Atazanavir | −77.870 | GLN189,TYR154 |
Amprenavir | −160.384 | TYR154,GLN127,VAL303 |
Fosamprenavir | −146.601 | CYS145,GLN189,SER144,GLY143,LEU141,HIS41,MET165,GLU166 |
Nelfinavir | −70.32 | CYS145,GLN189 |
Ritonavir | −101.440 | GLN189,THR24,CYS145 |
Indinavir | −98.704 | GLN189,GLY143,THR26 |
Saquinavir | −100.442 | GLU166,GLN189,THR26 |
The results of blind molecular docking between the standard antiviral protease inhibitors and the 3CLpro from
Graphical representation of the targeted molecular docking between the 3CLpro from
To date, the only approved anti-COVID-19 treatment is a repurposed antiviral drug (Remdesivir). Hence, drug repurposing might be an effective approach for accelerating drug discovery against COVID-19. Computational drug repositioning offers a noteworthy reduction in time and costs of new drug development and increases success rates in comparison to traditional methods. Therefore, to date, different computational methods such as data mining, machine learning, network analysis, and molecular docking have successfully been used for drug repurposing.
Molecular docking is a popular bioinformatics method that recently has been highly regarded for studying the drug ability of biological entities, protein-ligand interactions, mechanism action of drug candidates, and drug repositioning. Retrieval drug candidates from standard databases or previous reports, lead and target optimization, running the molecular docking process, and results analysis are the main steps in molecular docking-based drug repositioning. The binding affinity of a drug candidate to key amino acid(s) of the identified target molecule can be considered a decision factor in the drug repositioning process.
Despite the advantages of computational drug repositioning, studying drug-target interactions by in silico methods is still far from reality.
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\n\nSee a complete overview and description of the steps involved in the publishing process here.
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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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