Major Histocompatibility Complex Binding Peptides Research Articles

Targeted acidosis mediated delivery of antigenic MHC-binding peptides.

Cytotoxic T lymphocytes are the primary effector immune cells responsible for protection against cancer, as they target peptide neoantigens presented through the major histocompatibility complex (MHC) on cancer cells, leading to cell death. Targeting peptide-MHC (pMHC) complex offers a promising strategy for immunotherapy due to their specificity and effectiveness against cancer. In this work, we exploit the acidic tumor micro-environment to selectively deliver antigenic peptides to cancer using pH(low) insertion peptides (pHLIP). We demonstrated the delivery of MHC binding peptides directly to the cytoplasm of melanoma cells resulted in the presentation of antigenic peptides on MHC, and activation of T cells. This work highlights the potential of pHLIP as a vehicle for the targeted delivery of antigenic peptides and its presentation via MHC-bound complexes on cancer cell surface for activation of T cells with implications for enhancing anti-cancer immunotherapy.

Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks.

Simple SummaryMajor histocompatibility complex molecules are of significant biological and clinical importance due to their utility in immunotherapy. The prediction of potential MHC binding peptides can estimate a T-cell immune response. The variable length of existing MHC binding peptides creates difficulty for MHC binding prediction algorithms. Thus, we utilized a bilateral and variable long-short term memory neural network to address this specific problem and developed a novel MHC binding prediction tool.As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use.

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.

Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC binding are too expensive and time-consuming to meet the clinical needs. In this study, we developed an in silico method with a deep convolutional neural network (CNN) model, iConMHC, to predict peptide MHC binding affinity. Unlike other in silico methods that only learn from properties of amino acid in neoantigen peptides alone and/or MHCs alone, iConMHC learns from physical and chemical interaction properties between pairwise amino acids from the two molecules. These properties, such as contact potentials and distances in folded proteins, directly affect neoantigen-MHC binding affinity. In addition, IConMHC is a pan-allele model that is capable of making predictions for all the MHC alleles. Even for those rare MHC alleles without training data, iConMHC can make predictions with reasonable accuracy. We benchmarked iConMHC with other commonly used MHC-I binding predictors and found our model performs better than most of the pan-allele models.

Massively parallel interrogation and mining of natively paired human TCRαβ repertoires.

T cells engineered to express antigen-specific T cell receptors (TCRs) are potent therapies for viral infections and cancer. However, efficient identification of clinical candidate TCRs is complicated by the size and complexity of T cell repertoires and the challenges of working with primary T cells. Here, we present a high-throughput method to identify TCRs with high functional avidity from diverse human T cell repertoires. The approach uses massively parallel microfluidics to generate libraries of natively paired, full-length TCRαβ clones, from millions of primary T cells, which are then expressed in Jurkat cells. The TCRαβ-Jurkat libraries enable repeated screening and panning for antigen-reactive TCRs using peptide:MHC binding and cellular activation. We captured >2.9 million natively paired TCRαβ clonotypes from six healthy human donors and identified rare (<0.001% frequency) viral antigen–reactive TCRs. We also mined a tumor-infiltrating lymphocyte (TIL) sample from a melanoma patient and identified several tumor-specific TCRs, which, after expression in primary T cells, led to tumor cell killing.

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10-16), including CD8+ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

IDENTIFICATION OF MHC PEPTIDES USING MASS SPECTROMETRY FOR NEOANTIGEN DISCOVERY AND CANCER VACCINE DEVELOPMENT.

Immunotherapy with neoantigens presented by major histocompatibility complex (MHC) is one of the most promising approaches in cancer treatment. Using this approach, cancer vaccines can be designed to target tumor-specific mutations that are not found in normal tissues. Clinical trials have demonstrated an increased immune response and eradication of tumors after injecting synthetic peptides selected from the immunopeptidome. Although the sequence of MHC binding peptides can be predicted from genome sequencing and prediction algorithms, this approach results in large numbers of predicted peptides, requiring the confirmation by mass spectrometry (MS) analysis. Identification of MHC peptides by direct MS analysis of immunopeptidome is accurate and sensitive, with tens of thousands of unique peptides potentially identified from either cancer cell line or tumor tissue. Peptides with mutations can also be identified with patient-specific protein databases constructed from genome or transcriptome sequencing data. MS analysis also enables the characterization of the post-translational modifications (PTMs) of those antigens that cannot be predicted. Moreover, PTMs were found to be more efficient in triggering an immune response. In addition to reviewing recent advances in the identification of neoantigens using MS, the techniques for cancer vaccine candidate selection and formulation, vaccine delivery systems, and the potential for use in combination with other therapeutics are also discussed. It is anticipated that MS-based techniques will play an important role in future cancer vaccine development. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:110-125, 2021.

Neoantigen Dissimilarity to the Self-Proteome Predicts Immunogenicity and Response to Immune Checkpoint Blockade.

Despite improved methods for MHC affinity prediction, the vast majority of computationally predicted tumor neoantigens are not immunogenic experimentally, indicating that high-quality neoantigens are beyond current algorithms to discern. To enrich for neoantigens with the greatest likelihood of immunogenicity, we developed an analytic method to parse neoantigen quality through rational biological criteria across five clinical datasets for 318 cancer patients. We explored four quality metrics, including analysis of dissimilarity to the non-mutated proteome that was predictive of peptide immunogenicity. In patient tumors, neoantigens with high dissimilarity were unique, enriched for hydrophobic sequences, and correlated with survival after PD-1 checkpoint therapy in patients with non-small cell lung cancer independent of predicted MHC affinity. We incorporated our neoantigen quality analysis methodology into an open-source tool, antigen.garnish, to predict immunogenic peptides from bulk computationally predicted neoantigens for which the immunogenic "hit rate" is currently low.

Biosynthesis of Conjugate Vaccines Using an O-Linked Glycosylation System.

ABSTRACT Conjugate vaccines are known to be one of the most effective and safest types of vaccines against bacterial pathogens. Previously, vaccine biosynthesis has been performed by using N-linked glycosylation systems. However, the structural specificity of these systems for sugar substrates has hindered their application. Here, we report a novel protein glycosylation system (O-linked glycosylation via Neisseria meningitidis) that can transfer virtually any glycan to produce a conjugate vaccine. We successfully established this system in Shigella spp., avoiding the construction of an expression vector for polysaccharide synthesis. We further found that different protein substrates can be glycosylated using this system and that the O-linked glycosylation system can also effectively function in other Gram-negative bacteria, including some strains whose polysaccharide structure was not suitable for conjugation using the N-linked glycosylation system. The results from a series of animal experiments show that the conjugate vaccine produced by this O-linked glycosylation system offered a potentially protective antibody response. Furthermore, we elucidated and optimized the recognition motif, named MOOR, for the O-glycosyltransferase PglL. Finally, we demonstrated that the fusion of other peptides recognized by major histocompatibility complex class II around MOOR had no adverse effects on substrate glycosylation, suggesting that this optimized system will be useful for future vaccine development. Our results expand the glycoengineering toolbox and provide a simpler and more robust strategy for producing bioconjugate vaccines against a variety of pathogens.

MHC binding peptides for designing of vaccines against Japanese encephalitis virus: A computational approach.

Japanese encephalitis (JE), a viral disease has seen a drastic and fatal enlargement in the northern states of India in the current decade. The better and exact cure for the disease is still in waiting. For the cause an in silico strategy in the development of the peptide vaccine has been taken here for the study. A computational approach to find out the Major Histocompatibility Complex (MHC) binding peptide has been implemented. The prediction analysis identified MHC class I (using propred I) and MHC class II (using propred) binding peptides at an expectable percent predicted IC (50) threshold values. These predicted Human leukocyte antigen [HLA] allele binding peptides were further analyzed for potential conserved region using an Immune Epitope Database and Analysis Resource (IEDB). This analysis shows that HLA-DRB1*0101, HLA-DRB3*0101, HLA-DRB1*0401, HLA-DRB1*0102 and HLA-DRB1*07:01% of class II (in genotype 2) and HLA-A*0101, HLA-A*02, HLA-A*0301, HLA-A*2402, HLA-B*0702 and HLA-B*4402% of HLA I (in genotype 3) bound peptides are conserved. The predicted peptides MHC class I are ILDSNGDIIGLY, FVMDEAHFTDPA, KTRKILPQIIK, RLMSPNRVPNYNLF, APTRVVAAEMAEAL, YENVFHTLW and MHC class II molecule are TTGVYRIMARGILGT, NYNLFVMDEAHFTDP, AAAIFMTATPPGTTD, GDTTTGVYRIMARGI and FGEVGAVSL found to be top ranking with potential super antigenic property by binding to all HLA. Out of these the predicted peptide FVMDEAHFTDPA for allele HLA-A*02:01 in MHC class I and NYNLFVMDEAHFTDP for allele HLA-DRB3*01:01 in MHC class II was observed to be most potent and can be further proposed as a significant vaccine in the process. The reported results revealed that the immune-informatics techniques implemented in the development of small size peptide is useful in the development of vaccines against the Japanese encephalitis virus (JEV).

Computational Approach to Identify the Major Histocompatibility Complex Binding Antigenic Peptides from 'Ascariasis'

The Ascaris lumbricoides cause ‘ascariasis’ in human and its elimination is the major public concern. In this current investigation, we predicted the MHC (Major Histocompatibility Complex) class I & II binding peptides with computational approach like PSSM (Position Specific Scoring Matrices) and SVM (Support Vector Machine) algorithms. We predict the peptide binders of Cytochrome b (mitochondrion) protein from Ascaris lumbricoide sequence to MHC-I molecules are as 11mer_H2_Db, 10mer_H2_Db, 9mer_H2_Db, 8mer_H2_Db. Also study integrates prediction of peptide MHC class I binding; proteasomal C terminal cleavage and TAP transport efficiency by using sequence and properties of the amino acids. We also found the binding of peptides to different alleles by using Position Specific Scoring Matrix. Cytochrome B from Ascaris lumbricoides (365 residues long) with 357 nonamers having antigenic MHC binding peptides. PSSM based server will predict the peptide binders from sequence to MHCII molecules are as I_Ab.p, I_Ad.p, I_Ag7, which are found antigenic epitopes region in Cytochrome B from Ascaris lumbricoides. This investigation can be useful in rational vaccine design and simultaneously increase the understanding the role of the immune system against antigenic.

Prediction of Epitope-Based Peptides for Vaccine Development from Coat Proteins GP2 and VP24 of Ebola Virus Using Immunoinformatics

This study aims to design epitope-based peptides for the utility of vaccine development by targeting Glycoprotein 2 (GP2) and Viral Protein 24 (VP24) of the Ebola virus (EBOV) that, respectively, facilitate attachment and fusion of EBOV with host cells. Using various databases and tools, immune parameters of conserved sequences from GP2 and VP24 proteins of different strains of EBOV were tested to predict probable epitopes. Binding analyses of the peptides with major histocompatibility complex (MHC) class I and class II molecules, population coverage, and linear B cell epitope prediction were peroformed. Predicted peptides interacted with multiple MHC alleles and illustrated maximal population coverage for both GP2 and VP24 proteins, respectively. The predicted class-I nonamers, FLYDRLAST, LFLRATTEL and NYNGLLSSI were found to cover the maximum number of MHC I alleles and showed interactions with binding energies of −7.8, −8.5 and −7.7 kcal/mol respectively. Highest scoring class II MHC binding peptides were EGAFFLYDRLASTVI and SPLWALRVILAAGIQ with binding energies of −6.2 and -5.6 kcal/mol. Putative B cell epitopes were also found on 4 conserved regions in GP2 and two conserved regions in VP24. Our in silico analysis suggests that the predicted epitopes could be a better choice as universal vaccine component against EBOV irrespective of different strains and should be subjected to in vitro and in vivo analyses for further research and development.

Rapid, Precise, and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment.

The presentation of potentially pathogenic peptides by major histocompatibility complex (MHC) molecules is one of the most important processes in adaptive immune defense. Prediction of peptide-MHC (pMHC) binding affinities is therefore a principal objective of theoretical immunology. Machine learning techniques achieve good results if substantial experimental training data are available. Approaches based on structural information become necessary if sufficiently similar training data are unavailable for a specific MHC allele, although they have often been deemed to lack accuracy. In this study, we use a free energy method to rank the binding affinities of 12 diverse peptides bound by a class I MHC molecule HLA-A*02:01. The method is based on enhanced sampling of molecular dynamics calculations in combination with a continuum solvent approximation and includes estimates of the configurational entropy based on either a one or a three trajectory protocol. It produces precise and reproducible free energy estimates which correlate well with experimental measurements. If the results are combined with an amino acid hydrophobicity scale, then an extremely good ranking of peptide binding affinities emerges. Our approach is rapid, robust, and applicable to a wide range of ligand-receptor interactions without further adjustment.

Current challenges in peptide-based drug discovery.

The central event of each signaling step in biology is biomolecular recognition. Notwithstanding the importance of nucleic acids, carbohydrates, or lipids in ligand-target interactions, the effectors of most signal transduction processes are peptides. These can be fragments of proteins or stand-alone hormones, cytokines, toxins, antimicrobials, and many other types of peptides. At this point there is no good reason to classify peptides by the number of amino acid residues. We consider peptides as any polyamide (or even biopolymer with ester, thioester, or otherwise modified backbone) that can be made on a contemporary chemical peptide synthesizer. The limit in size is greater than the arbitrary cutoff of 50 amino acids set up by the US Food and Drug Administration (Carton and Strohl, 2013) for proteins and far exceeds that of biological recognition elements. While target recognition can occur with as low as a few residues (Ertl et al., 1991), even wide binding groves can be bound by 30–40 residue long peptides. Thus, in principle synthetic peptides can be used to regulate almost all receptor responses. The high specificity and low toxicity of peptide drugs derive from their extremely tight binding to their targets. This is due to the large chemical space the side-chain variations of native amino acids cover. Current databases estimate the total number of valid protein-ligand binding sites at 7700 (Khazanov and Carlson, 2013). Calculation based on 17 variable residues (Cys, Met, and Trp are significantly underrepresented in known ligands), show that an 83,000-member tetrapeptide library can be prepared that will essentially cover all unique protein binding regions. As the median length of an active site is 11 amino-acid residues (Khazanov and Carlson, 2013), designed ligands should also be longer. While historically six-residue positional scanning could identify ligands of receptors or epitopes of monoclonal antibodies (Dooley and Houghten, 1993), in our experience receptor agonists are 9–12 residue long (Otvos et al., 2008, 2011a) much like major histocompatibility complex binding peptides (Appella et al., 1995). Antagonists acting on the same receptor binding sites are somewhat shorter (vide infra). If it is assumed that conformational preferences improve the binding kinetics but only rarely thermodynamics, then the tremendous specificity of side-chain combinations of peptides over six residues in length can be even further expanded by using non-natural residues. Hundreds of appropriately protected and activated non-natural amino acid derivatives, ready for incorporation into synthetic peptides, are commercially available and indeed are frequently explored in peptidebased drug design. Importantly, chemical biology has provided both backbone and side-chain combinations for exploring an enormous chemical space and is expected to supply peptide chemists with further building blocks suitable for identifying close-to-ideal agonists and antagonists of any biologically important target. The selectivity of peptide drugs for their target is highlighted by the elevated success rate in clinical trials. According to a biotechnology report (Thomas, 2013), of the 40 approved drugs in 2012, five (12.5%) were peptides compared to 28 small molecule drugs and two monoclonal antibodies (in addition to three enzymes, a cell-based drug and a vaccine). However, in a recent report, the total number of peptide approvals between 2001 and 2012 was 19 (Kaspar and Reichert, 2013). Due to the low number of drug approvals, any particularly successful year can bias the ratios significantly. According to another report, the overall success rate of all drugs entering clinical trials is just 10.4% (Hay et al., 2014). Sixty-five percent of small molecules proceed from Phase I to Phase II in non-oncology applications, a figure identical for peptide/protein drugs. Interestingly peptides/proteins outperform small molecules at the Phase II → Phase III transition stage with 29% for small molecules and 42% for the larger drug candidates. While peptides have traditionally been considered safe in Phase I clinical trials, the public perception is that they are less beneficial in late clinical trials when they are compared side-by-side with different types of treatment modalities. It must be mentioned that peptides are less successful in oncology than in other applications. The cost of large scale peptide production might well-exceed those of small molecule drugs, but if one considers the total cost of the drug development process, the active pharmaceutical ingredient expense will remain under 3% (Otvos, 2014a). In direct opposition to concerns with expensive peptides, the increased clinical success rate, and thus, overall expense/approved drug ratio compared to small molecule chemical entities, make peptide drug development particularly attractive. The biochemical processes that activated receptors directly or indirectly regulate include protein phosphorylation, nucleic acid transcription, ion transport, and a series of enzyme activities (Yan

Customized predictions of peptide-MHC binding and T-cell epitopes using EPIMHC.

Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide–MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide–MHC binding is based on motif-profiles and here we show how to make them using EPIMHC (http://imed.med.ucm.es/epimhc/). EPIMHC is a database of T-cell epitopes and MHC-binding peptides that unlike any related resource provides a framework for computational vaccinology. In this chapter, we describe how to derive peptide–MHC binding motif-profiles in EPIMHC and use them to predict peptide–MHC binding and T-cell epitopes. Moreover, we show evidence that customization of peptide–MHC binding predictors can lead to enhanced epitope predictions.

T-Cells from HLA-B*57:01+ Human Subjects Are Activated with Abacavir through Two Independent Pathways and Induce Cell Death by Multiple Mechanisms

Susceptibility to abacavir hypersensitivity has been attributed to possession of the specific human leukocyte antigen allele HLA-B*57:01. HLA-B*57:01-restricted activation of CD8+ T-cells provides a link between the genetic association and the iatrogenic disease. The objectives of this study were to characterize the functionality of drug-responsive CD8+ T-cell clones generated from HLA-B*57:01+ drug-naive subjects and to explore the relationship between abacavir accumulation in antigen presenting cells and the T-cell response. Seventy-four CD8+ clones expressing different Vβ receptors were shown to proliferate and kill target cells via different mechanisms when exposed to abacavir. Certain clones were activated with abacavir in the absence of antigen presenting cells. Analysis of the remaining clones revealed two pathways of drug-dependent T-cell activation. Overnight incubation of antigen presenting cells with abacavir, followed by repeated washing to remove soluble drug, activated approximately 50% of the clones, and the response was blocked by glutaraldehyde fixation. In contrast, a 1 h antigen presenting cell pulse did not activate any of the clones. Accumulation of abacavir in antigen presenting cells was rapid (less than 1 h), and the intracellular concentrations were maintained for 16 h. However, intracellular abacavir was not detectable by mass spectrometry after pulsing. These data suggest that T-cells can be activated by abacavir through a direct interaction with surface and intracellular major histocompatibility complex (MHC) molecules. With the former, abacavir seemingly participates in the MHC T-cell receptor binding interaction. In contrast, the latter pathway likely involves MHC binding peptides displayed as a consequence of abacavir exposure, but not abacavir itself.

EpiGASVM – a New Technique for MHC Class-II Epitope Prediction

Identification of major histocompatibility complex binding peptides is an important step in the selection of T-Cell epitope candidates suitable for usage in new vaccines.The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines for infectious diseases and cancers. We have developed EpiGASVM, a new approach for the in silico prediction of MHC Class-II epitopes, by combining two artificial intelligence techniques namely: evolutionary algorithms and support vector machines. We have applied nine variations of EpiGASVM to a dataset of similarity-reduced benchmark data and we have calculated the prediction accuracy and the area under the receiver operating characteristic curve as measures of performance.The results indicate that EpiGASVM is a promising new technique that could provide researchers with a new tool for the in silico selection of candidate peptides that can be used in rational vaccine design.

Sensitive Quantitative Predictions of MHC Binding Peptides and Fragment Based Peptide Vaccines from Taenia crassiceps

Taenia crassiceps is a member of the Taenia genus. It is a tapeworm. It is related to Taenia solium, the pork tapeworm, and to Taenia saginata, the beef tapeworm. Its larvae eat tiny holes in the human retina, eventually detaching it. The life cycle is when an adult lays eggs inside a wild canine. Peptide fragments of antigen protein can be used to select nonamers for use in rational vaccine design and to increase the understanding of roles of the immune system in infectious diseases. Analysis shows MHC class II binding peptides of antigen protein from Taenia crassiceps are important determinant for protection of host form parasitic infection. In this assay, we used PSSM and SVM algorithms for antigen design and predicted the binding affinity of antigen protein having 72 amino acids, which shows 64 nonamers. Binding ability prediction of antigen peptides to major histocompatibility complex (MHC) class I & II molecules is important in vaccine development from Taenia crassiceps.

Prediction of Major Histocompatibility Complex Binding Peptides and Epitopes from Neurotoxin of Mesobuthustamulus

Prediction of Major Histocompatibility Complex Binding Peptides and Epitopes from Neurotoxin of Mesobuthustamulus

Prediction of Major Histocompatibility Complex Binding Peptides and Epitopes from Fatty-Acid-Binding Protein of the Human Blood Fluke Schistosoma Japonicum

Schistosoma japonicum are blood flukes of humans that cause chronic, highly debilitating diseases involving extensive liver damage. In the present study, fatty-acid-binding protein of the human blood fluke Schistosoma japonicum is being used to find out highly suitable MHC binding peptides and epitopes. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class in response to almost all antigens and it give effects on specific sites. Predicted MHC binding regions acts like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. Fragments identified through this approach tend to be high efficiency binders, in which larger percentage of their atoms are directly involved in binding as compared to larger molecules. Binding ability prediction of peptides to major histocompatibility complex (MHC) class I & II molecules is important in vaccine development from fatty-acid-binding protein of the human blood fluke Schistosoma japonicum.

A computational pipeline to generate MHC binding motifs.

Major histocompatibility complex (MHC) class I molecules play key roles in host immunity against pathogens by presenting peptide antigens to CD8+ T-cells. Many variants of MHC molecules exist, and each has a unique preference for certain peptide ligands. Both experimental approaches and computational algorithms have been utilized to analyze these peptide MHC binding characteristics. Traditionally, MHC binding specificities have been described in terms of binding motifs. Such motifs classify certain peptide positions as primary and secondary anchors according to their impact on binding, and they list the preferred and deleterious residues at these positions. This provides a concise and easily communicatable summary of MHC binding specificities. However, so far there has been no algorithm to generate such binding motifs in an automated and uniform fashion. In this paper, we present a computational pipeline that takes peptide MHC binding data as input and produces a concise MHC binding motif. We tested our pipeline on a set of 18 MHC class I molecules and showed that the derived motifs are consistent with historic expert assignments. We have implemented a pipeline that formally codifies rules to generate MHC binding motifs. The pipeline has been incorporated into the immune epitope database and analysis resource (IEDB) and motifs can be visualized while browsing MHC alleles in the IEDB.