Listing of peptide prediction tools available on the web [Accessed November 2017].
\r\n\t
",isbn:"978-1-83768-248-5",printIsbn:"978-1-83768-247-8",pdfIsbn:"978-1-83768-249-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"8bc7ffd7544fff1901301c787e64fada",bookSignature:"Prof. Magdy Elnashar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11998.jpg",keywords:"Preparation, Characterisation, Applications, Immobilised Cells, Biomaterials, Biofibers, Resins, Polysaccharides, Biocomposites in Health Sciences, Biocomposites in the Chemical Industry, Nanobiocomposites, Nano-Composites",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 27th 2022",dateEndSecondStepPublish:"July 29th 2022",dateEndThirdStepPublish:"September 27th 2022",dateEndFourthStepPublish:"December 16th 2022",dateEndFifthStepPublish:"February 14th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"24 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Prof. Magdy Elnashar received his M.Sc. 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His current position is the Head of Biopolymers & Nanobiotechnology Group at the Center of Excellence, National Research Center in Egypt. \nProf. Elnashar’s fields of interest are in the production of Nano to Macro Beads, Biopolymers Grafting, Immobilized Enzymes, Drug Delivery Systems, Nano Magnetic Particles, Diagnostic Kits (Immunology) and Water Purification.",institutionString:"Curtin University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Curtin University",institutionURL:null,country:{name:"Australia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"14",title:"Materials Science",slug:"materials-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"466998",firstName:"Dragan",lastName:"Miljak",middleName:"Anton",title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/466998/images/21564_n.jpg",email:"dragan@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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The presentation of self and nonself occurs through displaying cellular proteins on the cell surface by proteins of the major histocompatibility complex (MHC) gene cluster. In humans, the MHC locus is termed human leukocyte antigen (HLA) and comprises several gene loci with numerous different alleles for most of the genes [1]. One part of the genes is subsumed as HLA class I (HLA-I) with the gene products being expressed on virtually every nucleated cell in the human body. HLA-I molecules present peptides of intracellular proteins on the cell surface. Cytotoxic T cells (CD8+ T cells) as part of the adaptive immune system can recognize these peptide HLA-I (pHLA-I) complexes by the T-cell receptor (TCR) and scan simultaneously the HLA molecule and the peptide [2, 3] to discriminate between healthy and unhealthy cells, for example, virally infected cells. At the same time, natural killer (NK) cells that are part of the innate immune system scan the cell surface of HLA as well. These cells become activated when HLA-I is missing on the cell surface, for example, on virally infected or tumor cells [4].
\nPeptide loading on HLA-I is a complex mechanism and determines in addition to the HLA allele which peptides will be presented. The central part of this process is the peptide loading complex that is localized in the endoplasmic reticulum (ER). The HLA-I molecule, consisting of a heavy chain and a microglobulin, is stabilized by several chaperons since the structure is unstable when no peptide is bound. The transporter associated with antigen processing (TAP) imports protein fragments that are degraded in the cytosol by the proteasome into the ER. Depending on the sequence, these peptides are trimmed in different ways in the ER [5, 6]. The bridge between TAP and HLA-I is the chaperon tapasin (TPN) that facilitates peptide binding in the peptide binding groove [7].
\nBecause the HLA gene cluster ranks among the most polymorphic region in the human genome [1] and most of these polymorphisms are located in the peptide binding region (PBR) [8, 9], these polymorphisms result in an abundance of structurally different pHLA entities. In this chapter, we focus on the interplay between HLA alleles, bound peptides and the interaction with immune receptors. It is highlighted that even minor differences in the HLA sequence can impact on the bound ligand or the pHLA structure. Every single peptide changes the overall structure of the HLA molecule. Structural alterations that differ from self-pHLA structures will be recognized by the immune system. Therefore, the last parts of the chapter demonstrate the advantage of established immunopeptidomes for immunotherapies.
\nThe viability of the immune system is governed by interactions between effector cell receptors and their cognate antigenic ligands. Immune effector cells survey HLA-I molecules on the surface of antigen-presenting cells by indirectly scanning the proteomic content of every single cell. The fundamental role of CD8+ T cells, the elimination of pathogens, is elicited through HLA-I molecules complexed to a peptide of foreign (e.g., viral) origin.
\nPositive and negative selection of T cells in the thymus is a critical step for the development of a mature functional immune system. Immune cells that have not developed immune tolerance against specific pHLA-I complexes during thymus selection will recognize these antigens as foreign. The allele-specific and patient-specific peptides that are presented on the cell surface shape the individual immune response. Even a single alteration in the peptide sequence can be recognized by immune effectors. Single alterations in the sequence of the HLA heavy chain might not affect which peptide sequences can be bound but could lead to a modified overall pHLA-I structure or might affect the strength of peptide binding resulting in pHLA-I complexes with different half-life times. Besides the influence of amino acid (AA) exchanges within the heavy chain, peptides might undergo competition in patients who carry alleles with the same peptide-binding motif. For those reasons, the presentation of a given peptide is dependent on the HLA type and the health status of the patient. Half-life times of pHLA complexes influence the phenotype and/or functionality of T cells. That has to be taken into account when choosing pHLA-I targets for T-cell therapeutics. Based on these facts the importance of knowledge about allelic peptide specificity becomes obvious. Peptides have to pass several intracellular filters and processing steps before being presented to immune effectors cells. The bottleneck is the peptide loading complex (PLC). Not every available peptide in a cell would be necessarily bound to an HLA molecule and displayed at the cell surface since peptides undergo peptide competition during recruitment through the PLC. The PLC consists of several proteins; each has a specialized function for peptide selectivity and specificity. Proteins of this complex are dedicated targets for viral interference and thus viral immune evasion (Figure 1).
\nViral interference with HLA class I peptide presentation. Depicted are targets for viral interference with peptide presentation on HLA-I molecules. HLA-I maturation and surface presentation of peptides are blocked through different mechanisms early postinfection. Most viruses directly target peptide loading through TAP and peptide optimization by tapasin (HCMV [
The HLA heavy chain has to adopt a peptide-receptive form and complex with certain proteins of the PLC. It could be demonstrated that certain allelic variants interact differently with proteins of the PLC and thus are more prone to present peptides of malignant origin. Those HLA subtypes can differ from alleles that are strictly dependent on the association with the PLC for peptide loading only by a single amino acid within the heavy chain, altering the structural interface that interacts with the PLC [10, 11, 12]. Especially the interaction of TPN, a protein that mediates the binding of high-affinity peptides into the HLA-I PBR, with the HLA-I molecule, is of exquisite importance to produce stable pHLA-I complexes that persist on the cell surface. However, few allelic HLA variants are able to present peptides without the assistance of TPN. That enables those alleles to continuously present viral peptides at an infectious stage where viral interference with TPN occurs; however, the presentation of self-peptides that did not take part in negative T-cell selection would be facilitated and might lead to uncontrollable autoimmune reactions. HLA-I variants that select and load peptides without the assistance of TPN are likely to present a broad range of low-affinity viral-derived peptides during an infection. However, the presentation of viral peptides during an active viral infection is a rare event since still self-peptides are present intracellularly and would compete with viral peptides to fit into the PBR. The viral peptides that would reach the cell surface complexed to an HLA-I molecule could hardly be predicted by peptide prediction tools.
\nDue to the fact that peptides undergo different selection steps before being presented by an individual HLA allele, it has to become clear that the individual HLA profile is the major and most distinguished obstacle. Every HLA allele differs from another by the composition of AAs in and/or outside the peptide-binding groove, resulting in allele-specific profiles of the bound peptides [13, 14, 15]. Therefore, the knowledge of an individual peptide binding profile can be used as precedence for the measurement of permissivity between HLA subtypes. The sequence, length and immunogenicity of a given peptide determine the half-life time of the whole pHLA complex and furthermore the specificity and reactivity of their cognate immune receptor.
\nSeveral studies demonstrated the impact of the sequence and feature of HLA-bound peptides on receptor recognition. This observation holds true for T-cell receptors of the adaptive immune system as well as for NK-cell receptors of the innate immune system. We recently could highlight the potential of the nonclassical HLA-I molecule HLA-E to select and present peptides of extraordinary length and their effect on differential NK-cell recognition. For the nonpolymorphic HLA-E molecule only two functional variants exist distinguished by a single AA difference. That would imply that HLA-E is, in regard to its peptide profile, invariant. However, by sequencing their bound ligands, we found both alleles presenting a different set of peptides [16]. Since HLA-E is an intermediate molecule for the adaptive and innate immune system, supporting the non-PLC-dependent presentation of peptides during HLA downregulation episodes, the invariability of this molecule would certainly make biological sense. However, the finding that HLA-E subtypes differ in their immunity was somehow unexpected. Reconstitution of empty HLA-E molecules with the designated peptides on the surface of artificial APCs resulted in a peptide-specific immune recognition [17].
\nTwo types of NK-cell receptors interact with HLA-I molecules, killer cell immunoglobulin-like receptors (KIRs) and C-type lectin-like receptors. The former ones are highly polymorphic and polygenic in the population and recognize HLA-A, -B and -C alleles [18], whereas the latter ones bind among others to HLA-E [19, 20]. CD8+ T cells recognize endogenous HLA alleles and assess their immune status by virtue of the presented peptide. The display of different peptides thus allows for precise monitoring of the immune status of the cell through the adaptive immune system. However, this also means that these cells have to be primed on the recognition of specific peptides that are usually derived from endogenously expressed proteins [21]. The presented peptide repertoire can be altered by aberrant protein expression as well as the presence of foreign proteins (e.g., viral proteins) in the cell. To counter recognition by CD8+ T cells, many viruses have developed immune evasion strategies that specifically target HLA peptide loading and presentation. For instance, the HCMV protein US6 interferes with peptide translocation at the ER thus depriving the available peptide pool or even directly procures that the HLA heavy chain is degraded through US2 or US11 [22]. However, in the event of such disrupted HLA-I presentation, NK cells become activated. Although NK cells do not recognize the specific HLA allele, the absence of HLA expression on the cell surface triggers NK-cell activation. Nevertheless, NK cells can still recognize certain peptides in the context of the nonclassical HLA molecule HLA-E that presents a very narrow set of peptides derived from the signal sequence of other HLA class I molecules. These peptides are 9 AA in length and are anchored preferably by Met at peptide position p2 and Leu at pΩ, whereas positions p4, p5 and p6 are accessible to the solvent [23, 24]. On NK cells, HLA-E in combination with these peptides is recognized by inactivating the NKG2A/CD94 heterodimeric receptor complex [25]. In the absence of HLA class I molecules or during cell stress, HLA-E was shown to present noncanonical peptides of different length [16, 17], for example, the Hsp60-derived peptide QMRPVSRVL that causes loss of recognition by NKG2A/CD94 [26] or the HIV Gag-derived peptide AISPRTLNA that causes HLA-E upregulation [27]. In the case of HCMV, a peptide from the UL40 protein that closely resembles the sequence of leader peptides from certain HLA-C allotypes is provided to stabilize HLA-E expression in infected cells. However, in individuals negative for these HLA-C allotypes, the UL40-peptide constitutes the presentation of nonself on HLA-E and can thus elicit a CD8+ T-cell response [19, 28]. Additionally, HLA-E in complex with other pathogen-derived peptides was shown to stimulate CD8+ T-cell responses. For instance, the Epstein–Barr virus-derived peptide SQAPLPCVL was shown to be recognized by the αβTCR of a CD8+-CD94/NKG2C+ T-cell clone [29, 30] described HLA-E-restricted
To identify peptides that would be suitable for application in cellular therapeutic strategies, certain properties have to be analyzed: (1) the peptide-binding motif of the HLA allele of choice and (2) the HLA allele-specific features of the bound peptides such as length and topology. There are several bioinformatic tools that enable scientists and clinicians to predict peptides that would be presented by a certain HLA allele, yet, these tools do not consider allele-specific features and immune dominance of peptides. The kinetics of antigen expression and the competition of peptides to be preferentially bound and presented are also not considered by these bioinformatics prediction tools. Most data available in these tools are based on experimental peptide data (Tables 1 and 2). However, it remains unclear if those predicted peptides would ever be naturally presented. Peptides predicted from for example a viral protein would not necessarily be processed, selected and/or presented by the respective patient awaiting T-cell therapy. Therefore, the pathogen- or peptide-specific T cells that would be transplanted might not be able to find their mutual pHLA molecule. An example of the first successful adoptive transfer of virus-specific T cells described the transfer of HCMV-specific T cells and their reconstitution of antiviral immunity in an immune-deficient bone marrow transplant recipient [31]. The technique of adoptive T-cell transfer could be further improved leading to the selection of specific T-cells based on IFN-γ secretion or pHLA multimer staining and selection following antigen stimulation [32, 33, 34]. Both techniques bear the imperative to know which viral peptides are presented on the particular HLA subtype of, for example, HCMV-infected cells. So far, few HLA-restricted peptides have been studied. The majority of peptides are derived from the well-characterized phosphoprotein (pp)65 or the immediately early (IE)1 protein, however, not for every patient responses against these two proteins are immunodominant [35, 36]. Best studied are the pp65-derived peptides NLVPMVATV and TPRVTGGGAM, restricted to HLA-A*02:01 and HLA-B*07:02, respectively. Those peptides are described to induce extremely strong T-cell responses [37, 38, 39, 40, 41, 42]. Yet, these peptides have been computationally predicted [43] but not been isolated from HLA molecules. Thus, it remains unproven if they would ever be naturally presented. That might be an explanation for the failure of long-term T-cell transfers [33, 44, 45].
\nName | \nApplication | \nMethods | \nRef. | \nNumber of HLA class I alleles | \nNumber of HLA class II alleles | \nPeptide length | \nOther species | \nURL | \n
---|---|---|---|---|---|---|---|---|
BIMAS | \nPredicts half-time of dissociation of peptides from protein sequences | \nCoefficient tables | \n[58] | \n41 inc. supertypes | \n0 | \n8–10 | \nNo | \n\nhttps://www-bimas.cit.nih.gov/molbio/hla_bind/\n | \n
EpiJen | \nPredicts peptide binding from protein sequence (proteasome cleavage, TAP binding and MHC binding) | \nMulti-step algorithm | \n[59] | \n18 | \n0 | \n9 | \nNo | \n\nhttp://www.ddg-pharmfac.net/epijen/EpiJen/EpiJen.htm\n | \n
hla_a2_smm | \nPredicts binding affinity of peptides, high affinity HLA-A2 binding peptides from protein sequence and mutated peptides with higher affinity | \nSMM pair coefficients | \n[60] | \n1 | \n0 | \n9–10 | \nNo | \n\nhttps://zlab.bu.edu/SMM/\n | \n
IEDB T Cell Epitope Prediction Tools | \nPredicts T cell epitopes from proteins (MHC binding, processing and immunogenicity) | \nSeveral tools can be chosen | \n[61, 62, 63, 64] | \n77 | \nn/s | \nClass I:8–14 Class II: n/s | \nChimpanzee, cow, gorilla, macaque, mouse, pig, rat for MHC class I; mouse for MCH class II | \n\nhttp://tools.iedb.org/main/tcell/\n | \n
Mappp | \nPredicts antigenic peptides to be processed and presented by MHC class I from peptide or protein sequence | \nUses BIMAS or SYFPEITHI for binding prediction | \n[65] | \n35 inc. supertypes | \n0 | \n8–10 | \nMouse, cattle | \n\nhttp://www.mpiib-berlin.mpg.de/MAPPP/index.html\n \nhttp://www.mpiib-berlin.mpg.de/MAPPP/binding.html\n | \n
MHC2MIL | \nPredicts binding affinity of MHC-II peptides from protein sequence | \nMIL | \n[66] | \n0 | \n26 | \n9–25 | \nNo | \n\nhttp://datamining-iip.fudan.edu.cn/service/MHC2MIL/index.html\n | \n
MHC2PRED | \nPrediction of MHC class II binders | \nSVM | \n[67] | \n0 | \n38 inc. supertypes | \n9 | \nMouse | \n\nhttp://crdd.osdd.net/raghava/mhc2pred/index.html\n | \n
MHCBN | \nDatabase with information about allele specific MHC binding peptides, MHC nonbinding, TAP binding, TAP nonbinding peptides and T-cell epitopes | \nDatabase | \n[68, 69] | \nn/s | \nn/s | \nn/s | \n\n | \nhttp://crdd.osdd.net/raghava/mhcbn/index.html\n | \n
MHCMIR | \nPredicts binding affinity and levels of MHC-II peptides from peptide or protein sequence | \nMIR | \n[70] | \n0 | \n13 | \nAll | \nMouse | \n\nhttp://ailab.ist.psu.edu/mhcmir/predict.html\n | \n
MHCPRED | \nPredicts binding affinity of peptides to MHC class I and II molecules and to TAP from protein sequence and calculates binding affinity for heteroclitic peptides | \nAdditive method, partial least square regression | \n[71, 72, 73] | \n11 | \n3 | \n9 | \nMouse | \n\nhttp://www.ddg-pharmfac.net/mhcpred/MHCPred/\n \nhttp://www.ddg-pharmfac.net/mhcpred/MHCPred/pepLib.html\n | \n
MMBPred | \nPredicts mutated high affinity and promiscuous MHC class-I binding peptides from protein sequence, epitope enhancement, 1–3 AAs mutation of nonamer peptides | \nQM | \n[74] | \n40 inc. supertypes | \n0 | \n9 | \nRhesus macaque, mouse | \n\nhttp://crdd.osdd.net/raghava/mmbpred/\n | \n
MULTIPRED | \nPredicts binding of peptides to HLA class I and class II DR supertypes and individual genotypes | \nUses NetMHCpan and NetMHCIIpan | \n[75] | \n13 supertypes | \n13 supertypes | \n8–11 for HLA class I and genotype 9 for HLA class II | \nNo | \n\nhttp://cvc.dfci.harvard.edu/multipred2/index.php\n | \n
NetCTL | \nPredicts CTL epitopes in protein sequences (Cleavage, TAP transport, HLA class I binding) | \nANN | \n[76] | \n12 supertypes | \n0 | \n9 | \nNo | \n\nhttp://www.cbs.dtu.dk/services/NetCTL/\n | \n
NetMHC | \nPredicts binding of peptide to MHC class I molecules from peptide or protein sequence | \nANN | \n[19, 77] | \n81 (or 12 supertypes) | \n0 | \n8–14 | \nChimpanzee, rhesus macaque, mouse, cuttle, pig | \n\nhttp://www.cbs.dtu.dk/services/NetMHC/\n | \n
NetMHCcons | \nPredicts binding of peptides to any known MHC class I molecule from peptide or protein sequence | \nConsensus (NetMHC, NetMHCpan and PickPocket) | \n[78] | \nUser specified | \n0 | \n8–15 | \nChimpanzee, gorilla, rhesus macaque, mouse, cuttle, pig | \n\nhttp://www.cbs.dtu.dk/services/NetMHCcons/\n | \n
NetMHCII | \nPredicts binding of peptides to HLA-DR, HLA-DQ, HLA-DP from peptide or protein sequence | \nANN | \n[79, 80] | \n0 | \n26 | \nvariable | \nMouse | \n\nhttp://www.cbs.dtu.dk/services/NetMHCII/\n | \n
NetMHCIIpan | \nPredicts binding of peptides to HLA-DR, HLA-DQ, HLA-DP from peptide or protein sequence | \nANN | \n[81, 82] | \nUser specified | \n0 | \n\n | Mouse | \n\nhttp://www.cbs.dtu.dk/services/NetMHCIIpan/\n | \n
NetMHCpan | \nPredicts binding of peptides to any known MHC class I molecule from peptide or protein sequence | \nANN | \n[19, 83, 84] | \nUser specified | \n0 | \n8–14 | \nChimpanzee, gorilla, rhesus macaque, mouse, cuttle, pig | \n\nhttp://www.cbs.dtu.dk/services/NetMHCpan/\n | \n
nHLAPred: ANNPred | \nPredicts MHC Class I binding regions in proteins | \nANN | \n[85] | \n26 inc. supertypes | \n0 | \n\n | Mouse | \n\nhttp://crdd.osdd.net/raghava/nhlapred/neural.html\n | \n
nHLAPred: ComPred | \nPredicts MHC Class I binding regions in proteins | \nANN/QM | \n[85] | \n59 inc. supertypes | \n0 | \n\n | Rhesus macaque, mouse | \n\nhttp://crdd.osdd.net/raghava/nhlapred/comp.html\n | \n
PREDPEP | \nPredicts binding of peptides to HLA class I from peptide or protein sequence | \nPublished coefficient tables | \n[86] | \n6 | \n0 | \n8–10 (dependent on the allele) | \nMouse | \n\nhttp://margalit.huji.ac.il/Teppred/mhc-bind/index.html\n | \n
ProPred | \nPredicts MHC Class II binding regions in an antigen sequence | \nQM | \n[87] | \n51 | \n0 | \n\n | No | \n\nhttp://crdd.osdd.net/raghava/propred/\n | \n
ProPred I | \nPredicts MHC Class I binding regions in an antigen sequence | \nQM | \n[88] | \n39 inc. supertypes | \n0 | \n\n | Mouse, cattle | \n\nhttp://crdd.osdd.net/raghava/propred1/index.html\n | \n
Rankpep | \nPredicts binding of peptides to MHC class I and class II molecules from peptide or protein sequence | \nPSSM | \n[89, 90, 91] | \nn/s | \nn/s | \nDependent on the allele | \n\n | \nhttp://imed.med.ucm.es/Tools/rankpep.html\n | \n
svmhc | \nPredicts binding of peptides to MHC class I molecules from peptide or protein sequence | \nSVM, uses MHCPEP or SYFPEITHI | \n[92, 93] | \n31 | \n0 | \n8–10 (dependent on the allele) | \nNo | \n\nhttp://svmhc.bioinfo.se/svmhc//\n | \n
SYFPEITHI | \nDatabase of MHC ligands and peptide motifs and epitope prediction | \nMatrix/motif-based, published motifs | \n[94] | \n33 | \n6 | \n8–11 for HLA class I; 15 for HLA class II | \nNo | \n\nhttp://www.syfpeithi.de/\n | \n
TEPITOPEpan | \nPredicts tissue-specific binding of peptides to MHC class II molecules from peptide or protein sequence | \nPSSM | \n[95] | \n0 | \n50 | \n9–25 | \nNo | \n\nhttp://datamining-iip.fudan.edu.cn/service/TEPITOPEpan/index.html\n | \n
Listing of peptide prediction tools available on the web [Accessed November 2017].
Abbr. HMM = hidden Markov model, SVM = support vector machine, PSSM = position-specific scoring matrix, QM = quantitative matrices, ANN = artificial neuronal networks, SMM = stabilized matrix method, MIL = multiple instance learning, MIR = multiple instance regression, n/s = not specified.
Name | \nUnderlying database/data source | \nURL for matrices/training data | \n
---|---|---|
BIMAS | \nCoefficient tables deduced from the published literature by Dr. Kenneth Parker, Children’s Hospital Boston | \n\nhttps://www-bimas.cit.nih.gov/cgi-bin/molbio/hla_coefficient_viewing_page\n \nhttps://www-bimas.cit.nih.gov/molbio/hla_bind/hla_references.html\n | \n
EpiJen | \nAntiJen [96, 97], SYFPEITHI [94] | \n\nhttp://www.ddg-pharmfac.net/antijen/AntiJen/antijenhomepage.htm\n | \n
hla_a2_smm | \nBIMAS [58], SYFPEITHI [94], data described in Peters, Tong [60] | \n\nhttps://zlab.bu.edu/SMM/\n | \n
IEDB T Cell Epitope Prediction Tools | \nIEDB [61], Sette lab, Buus lab, uses diverse predictions methods (see webpage) | \n\nhttp://tools.iedb.org/mhci/download/\n \nhttp://tools.iedb.org/mhcii/download/\n | \n
Mappp | \nBIMAS [58], SYFPEITHI [94], coefficient tables deduced from the literature by Kenneth Parker, Children’s Hospital Boston | \n— | \n
MHC2MIL | \nData by Wang, Sidney [98] | \n— | \n
MHC2PRED | \nJenPep [19], MHCBN [68] | \n— | \n
MHCBN | \nMHCBN [68] | \n— | \n
MHCMIR | \nIEDB [61] | \n— | \n
MHCPRED | \nJenPep [19] | \n— | \n
MMBPred | \nMHCBN [68] | \n— | \n
MULTIPRED | \nSee NetMHCpan and NetMHCIIpan | \n— | \n
NetCTL | \nSee NetMHC | \n— | \n
NetMHC | \nTrained for 81 HLA alleles including HLA-A, -B, -C and –E, n/s | \n— | \n
NetMHCcons | \nIEDB [61] | \n— | \n
NetMHCII | \nData by [19] | \n\nhttp://www.cbs.dtu.dk/suppl/immunology/NetMHCII-2.0.php\n | \n
NetMHCIIpan | \nIEDB [61] | \n\nhttp://www.cbs.dtu.dk/suppl/immunology/NetMHCIIpan-3.0/\n | \n
NetMHCpan | \nIEDB [61], IMGT/HLA database [1] | \n— | \n
nHLAPred: ANNPred | \nMHCBN [68] | \n— | \n
nHLAPred: ComPred | \nMHCBN [68], BIMAS [58] | \n\nhttp://crdd.osdd.net/raghava/nhlapred/matrix.html\n | \n
PREDPEP | \nPairwise potential table by Miyazawa and Jernigan [99] | \n— | \n
ProPred | \nQMs by Sturniolo, Bono [95] | \n\nhttp://crdd.osdd.net/raghava/propred/page4.html\n | \n
ProPred I | \nBIMAS [58] and matrices by Ruppert, Sidney [100] and Sidney, Southwood [101] | \n\nhttp://crdd.osdd.net/raghava/propred1/matrices/matrix.html\n | \n
Rankpep | \nMHCPEP [102], SYFPEITHI [94], GenBank [103] | \n— | \n
svmhc | \nMHCPEP [102], SYFPEITHI [94] | \n\nhttp://www.cs.cornell.edu/people/tj/svm_light/\n | \n
SYFPEITHI | \nPublished literature | \n— | \n
TEPITOPEpan | \nn/s | \n\nhttp://datamining-iip.fudan.edu.cn/service/TEPITOPEpan/TEPITOPEpan.html\n | \n
Listing of the underlying databases/data sources for peptide binding prediction.
Abbr. n/s = not specified.
The analysis of the individual patient and cell-type-specific immunopeptidome can be realized through sequencing the HLA-bound peptides. It is imperative for all ongoing peptide studies and cellular therapies to find peptides that are (1) naturally presented by the distinct allele, (2) immunogenic for (at best) a public T-cell repertoire and (3) preferentially presented when different peptides are available. A study from Yaciuk et al. showed for example that the peptides isolated from HIV-infected T cells differ from predicted peptides and exhibit different T-cell reactions, factors that have to be considered in designing immunotherapies [46]. That information is only available after immunopeptidome analyses.
\nIn the past, different methods have been applied to answer these questions comprehensively. There are two reliable methods to determine peptide sequences from selected HLA alleles. First, membrane-bound HLA molecules from recombinant single-antigen-presenting cells [47, 48] or from donor cells [49, 50] can be captured by affinity chromatographic methods and the bound peptides isolated and sequenced by mass spectrometry. Second, the most realizable method is the soluble HLA technology [16, 51]. Vectors encoding for soluble forms of HLA molecules (Exon 1–4) are transfected or lentivirally transduced into the cell line of choice. An optional recombinant tag (e.g., V5 tag) engineered at the C-terminus of the protein enables specific purification of the recombinant HLA molecule of choice without the challenge of contamination by cellular-self-HLA molecules. Both methods have been compared by Scull et al. [52] and indicated as an equivalent for the determination of allele-specific peptides. Furthermore, Badrinath et al. [10] could demonstrate that sHLA molecules associate during peptide acquisition with the loading complex as well. These results prove evidence that the use of sHLA technology for understanding allele-specific peptide-binding motifs, the prerequisite for updating peptide prediction databases, is the most time- and cost-efficient implementation.
\nFor the development of tailor-made T-cell-based immunotherapeutic strategies, the identification of tumor-specific HLA ligands is imperative. The production of recombinant sHLA-expressing cells derived from various tissues of malignant origins would guide towards understanding immune dominance through peptide competition. One of the most innovative applications is the peptide fishing from tumor tissue. Immunological tolerance is mediated through T cells that are primed in the thymus by self-peptides. Therefore, the comprehensive knowledge of the HLA immunopeptidome from diseased cells is fundamental for the development of efficient immunotherapeutic strategies. The presentation of peptides depends on the health state of a patient. During infections, the expression of HLA molecules and thus peptide presentation, including presentation of self-peptides, is diminished through an immune escape mechanism of the invasive pathogen.
\nThe treatment of cancer represents a great challenge due to the fact that the vast majority of HLA-restricted peptides differs from tissue to tissue and is dependent on the tumor entity. For that reason, it becomes obvious how fundamentally important the knowledge of the tumor-specific peptidome is. For personalized cancer immunotherapies, the knowledge of naturally presented peptides [53] represents the exclusive possibility for therapeutical success. The analysis of the mutanome, the proteomic content of a diseased cell, includes the discovery of neo-antigens or post-translational-modified peptides and the avoidance of targeting self-antigens from healthy tissue. The results of such individual mutanomes might alter during the course of tumor progression [16]. In peptide vaccination trials, the use of multiple peptides in combination [54, 55] represents a useful method for targeting all MHC-presenting cells with the peptide of choice. Yet, since the cell type where the peptide(s) bind to cannot be traced, the rates of antitumor immune responses might differ from patient to patient and certain tumor cells where, for example, low MHC expression rates might remain undetected from the immune system. To achieve a comprehensive and precise analysis of presented tumor antigens, the method of antigen discovery and appropriate T-cell assay for knowledge of immunogenicity of the dedicated antigen for vaccination is the key factor [56, 57].
\nPeptide selection and presentation is an exquisite biological and immunological event. Every single peptide is a mirror of the health state of a distinct cell and determines the outcome of immune recognition and responses. For all cellular therapies, the knowledge of the HLA-subtype specific proteome is crucial for the utilization of ligand prediction tools, which have to be implemented where no experimental data are available, yet.
\nGlobal business has become more competitive than before. The technology and dynamic life increase the opportunities and risks for several firms. Accordingly, financial statements and financial analysis must be developed to assess the company’s performance relative to its past performance or relative to its industrial competitors [1, 2, 3, 4, 5, 6]. The financial statements are annual reports containing essential information about the firm, including income, cash flows, and current financial condition, illustrating the assets, liabilities, and owners’ equity. However, if the financial information is not analyzed well, it will not help the company’s success and management decision-making. In addition, a firm should be prepared for the uncertainties and opportunities in the future; therefore, the financial analysis can support oversight of the future business [3, 7, 8].
The financial analysis uses financial statements to evaluate the firm’s overall performance, assess the equity securities, value opportunities, and risk, grow company earnings, and increase the cash flow. This study discusses financial statements, the difference and similarities between US GAAP and IFRS, financial data collection, research methodology, and analysis. In addition, a case study of one of the recent international companies, which is Tesla Motors, will be explained, and financial analysis and results will be applied to it [9, 10].
The used financial analysis method is financial ratios analysis. In this research, the profitability ratio, liquidity ratio, leverage ratio, and activity ratio will be applied to the financial statement of Tesla Motors. This study aims to evaluate the financial position of Tesla Motors through ratios and formulas to analyze the efficiency and business risk of the enterprise.
A financial statement consists three main statements that provide essential details and information about the company’s performance—income statements, balance sheets, and cash flow statements. The statements are analyzed annually using financial analysis techniques to continuously compare the firm effectiveness with previous years and compare it with the competitors from the same industry [11, 12, 13].
The income statement is defined as the profit and loss statements representing the cost of sales, total operating expenses, net profit to the net sales over a certain period, and earnings per share. The cost of sales contains the cost of merchandise, production, materials purchase expenses, research and development costs, and total operating expenses, including administrative and distribution expenses. To increase the net profit of the firm, expenses must be decreased, and sales have to be increased. The return of investment, financial flexibility, operating capabilities, and risk are essential information gathered from the income statement. The firm’s overall performance is measured by the return of investment, where the enterprise’s ability to adapt to consequences and opportunities is defined as financial flexibility. Moreover, the ability to maintain operations at the desired level is considered the operating capability, and risk is defined as the uncertainty related to the firm’s future. In summary, an Income statement supports the stakeholders and managers in evaluating the past performance, predicting future performance, and reducing the risk and uncertainty in achieving future cash flows [14].
The balance sheet statement is referred to as the statement of financial position. The primary role of the balance sheet is to report the firm’s assets, “economic resources,” liabilities, “economic obligations,” and equity over a particular period where total assets should be equal to total liabilities and equity “residual claims of owners.” The assets are shown concerning its cash liquidity, and the liabilities are related to its maturity date. The balance sheet can be measured by several values based on the relevance and reliability of desired attributes—a one-time cost, present cost, present market value, net realizable value, and the current value of future cash flows. At a specific balance sheet date, the current or present cost is the cash required to attain the asset, whereas the current market value is the amount of cash gained from selling the asset. In addition, the net realizable value is represented as the cash obtained from the sale of a future asset. The benefit of the balance sheet is to gather information and data about obligations, resources, and net resources equity. As well as it supports predicting the time, cost amounts, potential, and uncertainty of future cash flows [14].
The cash flow statement is a classification of cash payments and cash receipts issued by financing, operating, and investing activities. Each firm prepares the cash flow statement annually and compares the current year with previous years to evaluate the overall performance and plan the organization’s expenditures. The information and details provided by the cash flow statement report to stakeholders, lenders, and investors are cash that comes from or is used in operating and financing activities and the change of cash, whether increasing or decreasing in a particular period. In addition, the statement of cash flow support making economic decisions about the firm. The financing activities related to a firm are treasury stock, which describes the reacquisition of earlier issued shares, stock issuance, dividends payment to stakeholders, debt financing, and debt repayment. Investing activities contain fixed assets, debt sale or purchase, and equity securities of entities. Additionally, the operating activities are related to manufacturing companies and the sale of goods [14].
The above three statements can be prepared in accordance with two types of the conceptual framework, which are The International Financial Reporting Standards (IFRS), which is used the worldwide, and the United States Generally Accepted Accounting Principles (US GAAP), which was used in the US but recently it has been used by some firms in the UK and India. Both representations have similarities and differences in finance and account aspects. Some differences and similarities in financial aspects are illustrated in the table below (Similarities and Differences A comparison of IFRS, US GAAP, and UK GAAP*, 2005) (see Table 1).
Financial Statement | IFRS | US GAAP |
---|---|---|
Income statement |
|
|
Balance sheet |
|
|
Cash flow statements |
|
|
IFRS and US GAAP conceptual frameworks: Similarities and differences in financial statements preparation.
Sources: The Author.
To evaluate firm performance, it is complimentary to analyze the presented data and compare it with historical data or/and other competitors from the same industry. Thus, the basis and elements of comparison must be clarified to ensure an entity’s excellent performance and effectiveness. Analytical techniques can assess the firm’s capabilities to generate and grow the cash flow and earnings. Additionally, it supports identifying the cash flow and earnings risks for current and future times.
For example, one of the main aspects of comparison is the firm profitability compared with other companies. In most cases, there will be differences between the companies in the firm size, presenting financial information or/and the currency of financial data. Therefore, comparing the firms based on the net income will provide the right and valuable results. An alternative methodology was created, a ratio analysis technique that expresses one value concerning another value that enables more sufficient and accurate comparison and results. Furthermore, performing the standard size of financial statements eliminate the size factor, which provides improper results.
Regarding the issue of currency differences that appear from comparing international companies, an alternative method rather than using ratio analysis is using global exchange rates and unifying the currency in financial status at the end of a particular period. In addition to that, the enterprise compares its performance over time. Using the ratio analysis, which is horizontal financial statements that compare the current year to a based year and implement the results as a graph, shows the significant changes in the firm’s effectiveness and performance [14].
The primary objectives of using ratio analysis are as follows:
Assess the past performance, evaluate the current financial position, and predict future opportunities and risks.
Support analysis to determine earnings and free cash flow.
Examine the firm’s financial flexibility and ability to provide the cash needed to grow the firm and meet the obligations in normal or unexpected circumstances.
Improve management’s ability to make better decisions related to enterprise growth (Henry, Robinson, and Van Greuning, n.d.).
Types of ratio Analysis:
The automobile industry is the producer of electric, hybrid, and gasoline-powered vehicles and one of the largest industries that affect the economy and culture of the world. Moreover, it opened a broader market area for many businesses and commerce by using vehicles in transporting people and goods. Based on the worldwide statistics, the leading countries for the production of passenger cars in 2018 are represented in the figure below. The total global sales of passenger cars reached 62 million vehicles in 2018, and the United States produced around 2.8 million vehicles. Accordingly, the US is considered one of the largest automobile markets in production and sales.
The most produced and selling brands of vehicles in the US automobile industry are Ford, Volkswagen, Toyota, Hyundai, and Chevrolet. All mentioned models are fuel-based vehicles where a new generation of alternative energy resources was developed in the US to produce and sell hybrid and electric vehicles. One of the leading global producers of electric cars is Tesla Motors. This research discusses an overview of Tesla Motors, methodology, and analysis of Tesla’s financial statements (see Figure 1) [15].
Leading countries for the production of cars in 2018 [
Tesla Motors is an international manufacturing automotive and energy company founded in 2003 and based in California, US. The company is founded by Martin Eberhard, Marc Tarpenning, Elon Musk, J. B. Straubel, and Ian Wright. The organization aims to establish a sustainable energy eco-system by creating affordable vehicles and building unique energy solutions like solar roofs, power walls, and power packs. Tesla’s automotive and energy solution enables the consumers to manage the generation, consumption, and storage of renewable energy. Tesla Motors achieved a financial turnover of around 21.5 billion US dollars in the fiscal year of 2018 and 45,000 employees in 30 worldwide branches.
Due to the massive competition in the automotive industry, the global economy affecting the business, and the competitive prices, Tesla Motors added a unique value to its customers by alternating fuel-based vehicles with electric vehicles. Although Tesla avoids the risk of increasing the oil prices, technological and political environments significantly impact Tesla vehicle prices. Therefore, the financial and non-financial performance of Tesla should be analyzed carefully to support in making critical decisions and to determine the future risk and potential of the company [17].
The historical financial information and data of Tesla Motors provide a better understanding of its financial position and cash flow forecast. Moreover, by comparing the annual financial reports, the created value of Tesla and performance relative to peers can be examined. The financial information contains annual reports of Tesla’s income statement, balance sheet, and cash flow. Those financial details are authenticated and published by Tesla Motors company. In this research, the financial data duration will be analyzed, including the years from 2015 to 2018. A copy of detailed Tesla financial statements is attached in appendix A. Furthermore, the model used to evaluate Tesla’s financial performance is described in (Figure 2).
The research methodology.
The financial technique used in this research is the ratios analysis technique, which provides financial measurements and results to indicate the performance of Tesla Motors. The main four ratios for financial data analysis are liquidity ratios, assets management ratios, profitability ratios, and debt management ratios (Appendix B). Each ratio contains various formulas that describe an essential principle of finance and account, represented in the table below (see Table 2).
Liquidity Ratios | Asset Management Ratios | Profitability Ratios | Debt Management Ratios |
---|---|---|---|
1. Current Ratio | 1. Accounts Receivable Turnover | 1. Net Profit Margin | 1. Debt Ratio |
2. Quick Ratio | 2. Inventory Turnover Ratio | 2. Gross Profit Margin Ratio | 2. Time Interest Earned |
3. Cash Ratio | 3. Accounts Payable Turnover | 3. Operating Profit Margin |
Financial ratios analysis.
The liquidity ratio indicates the strong ability to use its asset to cover its short-term debts. The three liquidity ratios used in this research are current ratio, quick ratio, acid test, and cash ratio.
The current ratio formula is performed by dividing the current assets by the current liabilities for the same year. The current asset consists of cash and cash equivalents, restricted cash, net accounts receivable, inventory, prepaid expenses, and other current assets, where current liability includes Accounts payable, accrued liabilities, deferred revenue, resale value guarantee, customer deposits, current portion of long-term debt and capital leases (see Table 3 and Figure 3).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Current Assets | $ 8,306,308 | $ 6,570,520 | $ 6,259,796 | $ 2,782,006 |
Current Liability | $ 9,992,136 | $ 7,674,670 | $ 5,827,005 | $ 2,811,035 |
Current Ratio | 0.8313 | 0.8561 | 1.0743 | 0.9897 |
Current ratio analysis.
Current ratio graph.
The acid test or quick ratio is calculated by eliminating the inventories from current assets and dividing them by current liabilities (see Table 4 and Figure 4).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
(Current Assets-Inventories) | $ 5,192,862 | $ 4,306,983 | $ 4,192,342 | $ 1,594,168 |
Current Liability | $ 9,992,136 | $ 7,674,670 | $ 5,827,005 | $ 2,811,035 |
Acid Test Ratio | 0.5197 | 0.5612 | 0.7195 | 0.5671 |
Acid test ratio.
Acid test ratio graph.
A cash ratio is a type of measurement, which evaluates the strong ability to cover its current liability by only its cash and cash equivalent (see Table 5 and Figure 5).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Cash and Cash Equivalent | $ 3,685,618 | $ 3,367,914 | $ 3,393,216 | $ 1,196,908 |
Current Liability | $ 9,992,136 | $ 7,674,670 | $ 5,827,005 | $ 2,811,035 |
Cash Ratio | 0.3689 | 0.4388 | 0.5823 | 0.4258 |
Cash ratio.
Cash ratio graph.
The most important financial ratios for the manufacturing company are asset management because it effectively measures the enterprise usage and control of its assets. It consists many ratios, but in this research, the accounts receivable turnover, inventory turnover, accounts Payable turnover, and total asset turnover will be implemented on General Motors’ financial statements.
The accounts receivable turnover measures the number of cash collection times during a particular period, and it is calculated by dividing the sales by the average account receivable (see Table 6 and Figure 6).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Average Accounts Receivable | $ 949,022 | $ 515,381 | $ 499,142 | $ 168,965 |
Accounts Receivable Turnover Ratio | 22.61 | 22.82 | 14.02 | 23.95 |
Accounts receivable turnover ratio.
Accounts receivable turnover ratio graph.
This ratio is calculated several times inventories are sold and restocked yearly. All manufacturers have Inventories to keep unsold stocks which cost them significant value until the materials are sold out. It is measured by dividing the cost of goods sold over the average inventories (see Table 7 and Figure 7).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Cost of Goods Sold | $ 17,419,247 | $ 9,536,264 | $ 5,400,875 | $ 3,122,522 |
Average Inventories | $ 2,688,491 | $ 2,165,495 | $ 1,672,646 | $ 1,115,756 |
Inventory Turnover Ratio | 6.48 | 4.40 | 3.23 | 2.80 |
Inventory turnover ratio.
Inventory turnover ratio graph.
Since raw materials are considered the main expenses of manufacturing firms, the accounts payable turnover measures the speed of paying the purchasing of raw materials or inventories on the account. The account payable turnover is calculated by dividing the purchases over average accounts payable. The below formula calculates the value of the purchase (see Table 8 and Figure 8).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Purchases | $ 18,269,156 | $ 9,732,347 | $ 6,190,491 | $ 3,446,685 |
Average Accounts Payable | $ 3,404,451 | $ 2,390,250 | $ 1,860,341 | $ 916,148 |
Accounts Payable Turnover Ratio | 5.366 | 4.072 | 3.328 | 3.762 |
Accounts payable turnover ratio.
Accounts payable turnover ratio graph.
Purchases = Cost of goods sold + [(Ending inventory) – (Beginning inventory)].
The company’s overall efficiency and performance are evaluated by the profitability ratio, where it concentrates on measuring the assets and controlling the expenses to generate a reasonable rate of return. In addition, it analyses the firm current operational performance compared to previous years. The net profit margin, gross profit margin ratio, and operating profit margin ratio will be performed on the financial statements of Tesla Motors.
The net profit margin is calculated by dividing the net profit after tax over the net sales. For any automotive company, the higher the net profit margin, the better the performance (see Table 9 and Figure 9).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Net Profit after Tax | $ 1,062,582 | $ 2,240,578 | $ 773,046 | $ 888,663 |
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Net Profit Margin (Percentage) | 4.951% | 19.055% | 11.043% | 21.964% |
Net profit margin.
Net profit margin graph.
A gross profit margin serves as the source of paying additional expenses and savings for the future to assess financial health. The gross profit margin ratio is calculated by dividing the gross profit over sales (see Table 10 and Figure 10).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Gross Profit Margin | $ 1,004,745 | $ 2,209,032 | $ 746,348 | $ 875,624 |
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Gross Profit Margin Ratio (Percentage) | 4.682% | 18.786% | 10.662% | 21.642% |
Gross profit margin ratio.
Gross profit margin ratio graph.
This ratio is calculated by dividing the operating profits over sales (see Table 11 and Figure 11).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Operating Profits | $ 388,073 | $ 1,632,086 | $ 667,340 | $ 716,629 |
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Operating Profit Margin Ratio | 0.018 | 0.139 | 0.095 | 0.177 |
Operating profit margin ratio.
Operating profit margin ratio figure.
The degree of safety afforded to creditors is financial leverage or debt financing. There are two methods to obtain the enterprise debt by determining the borrowed funds used to finance assets on the balance sheet. The other is by obtaining the fixed charges covered by the operating profits in the income statement.
The debt ratio is calculated by dividing total debt over total assets, where total debt contains current liabilities and long-term debt (see Table 12 and Figure 12).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Total Debt | $ 13,433,874 | $ 15,348,310 | $ 10,923,162 | $ 4,125,915 |
Total Assets | $ 29,739,614 | $ 28,655,372 | $ 22,664,076 | $ 8,067,939 |
Debt Ratio | 0.452 | 0.536 | 0.482 | 0.511 |
Debt ratio.
Debt ratio graph.
The time interest earned is measured by dividing the earnings “EBIT” before interest tax by the interest charged. The ratio indicates the enterprise’s ability to meet the interest payment (see Table 13 and Figure 13).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
EBIT | $ 4,340,986 | $ 2,208,596 | $ 1,600,685 | $ 917,671 |
Interest Charges | $ 663,071 | $ 471,259 | $ 198,810 | $ 118,851 |
Time Interest Earned Ratio | 6.547 | 4.687 | 8.051 | 7.721 |
Time interest earned ratio.
Time interest earned graph.
An overview of financial statements, financial presentation methods, and financial analysis was discussed. A real-life case study on Tesla Motors was implemented to perform the financial analysis and concluded the results of its financial statements and analyses to evaluate its performance.
This study concludes that Tesla Motors continuously suffers from losses. Tesla Motors has a high value of assets since they concentrate on adding value to the customers and inventing unique electric vehicles. In addition, the automobile industry is too competitive where vehicle manufacturers compete to drive the attention of various stakeholders in the market. Furthermore, the new idea of shifting from fuel-based vehicles to electric-based vehicles needs significant duration to convince stakeholders to purchase the developed electric cars. However, this research proves that Tesla Motors made low gross profits where it decreased from 21.642% in 2015 to 4.682% in 2018. The decrement is due to high maintenance costs, research and development cost, selling expenses, and administrative expenses. Furthermore, the interest percentage is too high where Tesla Motors is accumulating the losses, which leads to increasing the interest expenses of the current year. The financial ratios support Tesla Motors to highlight the current firm position and provide the potential threats and opportunities in the future.
This study concludes that Tesla has changed their strategy to become the most worldwide sales of purely battery electric vehicles, capturing 23% of the market and 16% of the plug-in electric battery in the market for 2020. It has also developed a significant installer of photovoltaic systems through its subsidiary Tesla Energy in the United States. One of the largest global battery energy-storage systems suppliers is Tesla Energy, with 3.99 gigawatt-hours (GWh) installed in 2021.
This study also concludes that Tesla has changed its production strategy over time. It started to produce its first car model, the Roadster sports car, in 2009, which was followed by the Model S sedan in 2012, the Model X SUV in 2015, the Model 3 sedan in 2017, and the Model Y crossover in 2020. However, the Model 3 is the best-selling plug-in electric car in the global market, and, in the mid of 2021, it became the first electric car sale with 1 million units globally. The sale strategy thus has been developed. The global sales of Tesla increased to 936,222 cars in 2021, with an 87% increase over the previous year, and cumulative sales for all years totaled 2.3 million cars at the end of 2021. By the end of 2021, The market capitalization of Tesla reached $1 trillion to hold the rank 6 in US market history.
The year 2017–2018
The year 2017–2016
The year 2016–2015
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Shamshiri"}]},{id:"850",doi:"10.5772/5452",title:"Real-Time Evolutionary Algorithms for Constrained Predictive Control",slug:"real-time_evolutionary_algorithms_for_constrained_predictive_control",totalDownloads:2340,totalCrossrefCites:0,totalDimensionsCites:5,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Mario Luca Fravolini, Antonio Ficola and Michele La Cava",authors:null},{id:"847",doi:"10.5772/5449",title:"Cellular Non-Linear Networks as a New Paradigm for Evolutionary Robotics",slug:"cellular_non-linear_networks_as_a_new_paradigm_for_evolutionary_robotics",totalDownloads:2508,totalCrossrefCites:2,totalDimensionsCites:5,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Eleonora Bilotta and Pietro Pantano",authors:null},{id:"871",doi:"10.5772/5473",title:"Evolutionary Motion Design for Humanoid Robots",slug:"evolutionary_motion_design_for_humanoid_robots",totalDownloads:2534,totalCrossrefCites:2,totalDimensionsCites:4,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Toshihiko Yanase and Hitoshi Iba",authors:null}],mostDownloadedChaptersLast30Days:[{id:"63775",title:"Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Oil Palm Plantations",slug:"fundamental-research-on-unmanned-aerial-vehicles-to-support-precision-agriculture-in-oil-palm-planta",totalDownloads:2927,totalCrossrefCites:6,totalDimensionsCites:27,abstract:"Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. 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Shamshiri"}]},{id:"869",title:"Emotional Intervention on Stigmergy Based Foraging Behaviour of Immune Network Driven Mobile Robots",slug:"emotional_intervention_on_stigmergy_based_foraging_behaviour_of_immune_network_driven_mobile_robots",totalDownloads:2028,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Diana Tsankova",authors:null},{id:"64615",title:"Multimodal Classification of Mangoes",slug:"multimodal-classification-of-mangoes",totalDownloads:1022,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Grading, sorting, and classification of agricultural products are important steps to ensure a profitable and sustainable food industry. Human-intensive labors are replaced with better devices/machines that can be used in-line and generate sufficiently fast measurements for a high production volume. Most previous works focused on only one of the external quality parameters, such as color, size, mass, shape, and defects. In this work, we proposed an integrated machine vision system that can grade, sort, and classify mangoes using multiple features including weight, size, and external defects. We found that weight estimation using our proposed algorithm based on visual information was not statistically different from that of a conventional weight measurement using a static digital load cell; the estimation error is relatively small (4–5%). We also constructed an artificial neural network model to classify mango having multiple types of external defect; the classification error is less than 8% for the worst possible case. The results indicate that our system shows a great potential to be used in a real industrial setting. Future work will aim to investigate other features such as ripeness and bruises to increase the effectiveness and practicality of the system.",book:{id:"7270",slug:"agricultural-robots-fundamentals-and-applications",title:"Agricultural Robots",fullTitle:"Agricultural Robots - Fundamentals and Applications"},signatures:"Son V.T. Dao",authors:[{id:"252669",title:"Dr.",name:"Vu Truong Son",middleName:null,surname:"Dao",slug:"vu-truong-son-dao",fullName:"Vu Truong Son Dao"}]},{id:"62785",title:"Hybrid-Powered Autonomous Robots for Reducing Both Fuel Consumption and Pollution in Precision Agriculture Tasks",slug:"hybrid-powered-autonomous-robots-for-reducing-both-fuel-consumption-and-pollution-in-precision-agric",totalDownloads:1052,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Environmental contamination and the resulting climate change are major concerns worldwide. Agricultural vehicles that use fossil fuels emit significant amounts of atmospheric pollutants. Thus, this study investigates techniques to reduce fuel consumption in robotic vehicles used for agricultural tasks and therefore reduce atmospheric emissions from these automated systems. A hybrid energy system for autonomous robots devoted to weed and pest control in agriculture is modeled and evaluated, and its exhaust emissions are compared with those of an internal combustion engine-powered system. Agricultural implements require power for hydraulic pumps and fans; this energy is conventionally provided by power take-off (PTO) systems, which waste substantial amounts of energy. In this work, we examine a solution by designing and assessing a hybrid energy system that omits the alternators from the original vehicle and modifies the agricultural implements to replace the PTO power with electrical power. The hybrid energy system uses the original combustion engine of the tractor in combination with a new electrical energy system based on a hydrogen fuel cell. We analyze and compare the exhaust gases resulting from the use of (1) an internal combustion engine as the single power source and (2) the hybrid energy system. The results demonstrate that the hybrid energy system reduced emissions by up to approximately 50%.",book:{id:"7270",slug:"agricultural-robots-fundamentals-and-applications",title:"Agricultural Robots",fullTitle:"Agricultural Robots - Fundamentals and Applications"},signatures:"Mariano Gonzalez-de-Soto, Luis Emmi and Pablo Gonzalez-de-Santos",authors:[{id:"252783",title:"Prof.",name:"Pablo",middleName:null,surname:"Gonzalez-De-Santos",slug:"pablo-gonzalez-de-santos",fullName:"Pablo Gonzalez-De-Santos"},{id:"252784",title:"Dr.",name:"Mariano",middleName:null,surname:"Gonzalez-De-Soto",slug:"mariano-gonzalez-de-soto",fullName:"Mariano Gonzalez-De-Soto"},{id:"252785",title:"Dr.",name:"Luis",middleName:null,surname:"Emmi",slug:"luis-emmi",fullName:"Luis Emmi"}]},{id:"62821",title:"An Evaluation of Three Different Infield Navigation Algorithms",slug:"an-evaluation-of-three-different-infield-navigation-algorithms",totalDownloads:1116,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"In this chapter, we present and evaluate three different infield navigation algorithms, based on the readings from a LIDAR sensor. All three algorithms are tested on a small field robot and used to autonomously drive the robot between the two adjacent rows of maze plants. The first algorithm is the simplest one and just takes distance readings from the left and right side. If robot is not in the center of the mid-row space, it adjusts its course by turning the robot in the right direction accordingly. The second approach groups the left and right readings into two vertical lines by using least-square fit approach. According to the calculated distance and orientation to both lines, it adjusts the course of the robot. The third approach tries to fit an optimal triangle between the robot and the plants, revealing the most optimal one. Based on its shape, the course of the robot is adjusted. All three algorithms are tested in a simulated (ROS stage) and then in an outdoor (maze test field) environment comparing the optimal line with the actual calculated position of the robot. The tests prove that all three approaches work with an error of 0.041 ± 0.034 m for the first algorithm, 0.07 ± 0.059 m for the second, and 0.078 ± 0.055 m error for the third.",book:{id:"7270",slug:"agricultural-robots-fundamentals-and-applications",title:"Agricultural Robots",fullTitle:"Agricultural Robots - Fundamentals and Applications"},signatures:"Peter Bernad, Peter Lepej, Črtomir Rozman, Karmen Pažek and Jurij Rakun",authors:[{id:"179642",title:"Prof.",name:"Karmen",middleName:null,surname:"Pažek",slug:"karmen-pazek",fullName:"Karmen Pažek"},{id:"188886",title:"Prof.",name:"Črtomir",middleName:null,surname:"Rozman",slug:"crtomir-rozman",fullName:"Črtomir Rozman"},{id:"255090",title:"Dr.",name:"Jurij",middleName:null,surname:"Rakun",slug:"jurij-rakun",fullName:"Jurij Rakun"},{id:"255091",title:"Dr.",name:"Peter",middleName:null,surname:"Lepej",slug:"peter-lepej",fullName:"Peter Lepej"},{id:"255092",title:"BSc.",name:"Peter",middleName:null,surname:"Bernard",slug:"peter-bernard",fullName:"Peter Bernard"}]}],onlineFirstChaptersFilter:{topicId:"1283",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:319,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:16,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. 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His fields of interest are anterior segment disease, keratoconus, glaucoma, corneal dystrophies, and cataracts. His research topics include\nintraocular lens power calculation, eye modification induced by refractive surgery, glaucoma progression, and validation of new diagnostic devices in ophthalmology. \nHe has published more than 100 papers in international and Italian scientific journals, more than 60 in journals with impact factors, and chapters in international and Italian books. He has also edited two international books and authored more than 150 communications or posters for the most important international and Italian ophthalmology conferences.",institutionString:'University of Campania "Luigi Vanvitelli"',institution:{name:'University of Campania "Luigi Vanvitelli"',institutionURL:null,country:{name:"Italy"}}}]},{type:"book",id:"7560",title:"Non-Invasive Diagnostic Methods",subtitle:"Image Processing",coverURL:"https://cdn.intechopen.com/books/images_new/7560.jpg",slug:"non-invasive-diagnostic-methods-image-processing",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Mariusz Marzec and Robert Koprowski",hash:"d92fd8cf5a90a47f2b8a310837a5600e",volumeInSeries:3,fullTitle:"Non-Invasive Diagnostic Methods - Image Processing",editors:[{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",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. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. 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. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"bookSubject",path:"/subjects/1283",hash:"",query:{},params:{id:"1283"},fullPath:"/subjects/1283",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()