Prediction of promiscuous peptides that bind HLA class I molecules

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, MULTIPRED, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. MULTIPREDis both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. MULTIPREDreplaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.     

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

MOTIVATION: Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules. RESULTS: We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.     

Prediction of HLA-A2 binding peptides using Bayesian network

Prediction of peptides binding to HLA (human leukocyte antigen) finds application in peptide vaccine design. A number of statistical and structural models have been developed in recent years for HLA binding peptide prediction. However, a Bayesian Network (BNT) model is not available. In this study we describe a BNT model for HLA-A2 binding peptide prediction. It has been demonstrated that the BNT model allows up to 99 % accurate identification of the HLA-A2 binding peptides and provides similar prediction accuracy compared to HMM (Hidden Markov Model) and ANN (Artificial Neural Network). At the same time, it has been shown that the BNT has that advantage that it allows more accurate performance for smaller sets of empirical data compared to the HMM and the ANN methods. When the size of the training set has been reduced to 40% from the original data, the identification of the HLA-A2 binding peptides by the BNT, ANN and HMM methods produced ARoc (area under receiver operating characteristic) values 0.88, 0.85, 0.85 respectively. The results of the work demonstrate certain advantages of using the Bayesian Networks in predicting the HLA binding peptides using smaller datasets.     

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.     

Random screening of proteins for HLA-A*0201-binding nine-amino acid peptides is not sufficient for identifying CD8 T cell epitopes recognized in the context of HLA-A*0201

HLA-A2 is the most frequent HLA molecule in Caucasians with HLA-A*0201 representing the most frequent allele; it was also the first human HLA allele for which peptide binding prediction was developed. The Bioinformatics and Molecular Analysis Section of the National Institutes of Health (BIMAS) and the University of Tübingen (Syfpeithi) provide the most popular prediction algorithms of peptide/MHC interaction on the World Wide Web. To test these predictions, HLA-A*0201-binding nine-amino acid peptides were searched by both algorithms in 19 structural CMV proteins. According to Syfpeithi, the top 2% of predicted peptides should contain the naturally presented epitopes in 80% of predictions (www.syfpeithi.de). Because of the high number of predicted peptides, the analysis was limited to 10 randomly chosen proteins. The top 2% of peptides predicted by both algorithms were synthesized corresponding to 261 peptides in total. PBMC from 10 HLA-A*0201-positive and CMV-seropositive healthy blood donors were tested by ex vivo stimulation with all 261 peptides using crossover peptide pools. IFN-gamma production in T cells measured by CFC was used as readout. However, only one peptide was found to be stimulating in one single donor. As a result of this work, we report a potential new T cell target protein, one previously unknown CD8-T cell-stimulating peptide, and an extensive list of CMV-derived potentially strong HLA-A*0201-binding peptides that are not recognized by T cells of HLA-A*0201-positive CMV-seropositive donors. We conclude that MHC/peptide binding predictions are helpful for locating epitopes in known target proteins but not necessarily for screening epitopes in proteins not known to be T cell targets.     

Knowledge-based virtual screening of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus genome

A novel knowledge-based method is developed to virtually screen potential HLA-A?0201 binders from large-scale peptide candidates. This method utilizes the information from both the crystal structures and experimental affinities of various peptides bound with HLA-A*0201 to construct a single-position mutation free energy profile for accurately characterizing HLA-A*0201-peptide interaction and for effectively predicting the binding affinities of peptides to HLA-A*0201. We employ this method to analyze physicochemical properties and structural implication underlying the specific recognition and association between the HLA-A*0201 and a large panel of peptide segments generated from the herpes simplex virus type 1 (HSV-1) genome, and to evaluate the binding potencies of these peptide candidates to HLA-A*0201. As a result, 288 out of 38,020 candidates are predicted as the potential high-affinity binders of HLA-A*0201, from which three most promising peptides are picked out for further development of potent vaccines against HSV-1. In addition, we also demonstrate that this newly proposed method can successfully identify 8 known binders and 3 known nonbinders from the glycoproteins D and K of HSV-1.     

Tapasin discriminates peptide-human leukocyte antigen-A*02:01 complexes formed with natural ligands

A plethora of peptides are generated intracellularly, and most peptide-human leukocyte antigen (HLA)-I interactions are of a transient, unproductive nature. Without a quality control mechanism, the HLA-I system would be stressed by futile attempts to present peptides not sufficient for the stable peptide-HLA-I complex formation required for long term presentation. Tapasin is thought to be central to this essential quality control, but the underlying mechanisms remain unknown. Here, we report that the N-terminal region of tapasin, Tpn(1-87), assisted folding of peptide-HLA-A*02:01 complexes according to the identity of the peptide. The facilitation was also specific for the identity of the HLA-I heavy chain, where it correlated to established tapasin dependence hierarchies. Two large sets of HLA-A*02:01 binding peptides, one extracted from natural HLA-I ligands from the SYFPEITHI database and one consisting of medium to high affinity non-SYFPEITHI ligands, were studied in the context of HLA-A*02:01 binding and stability. We show that the SYFPEITHI peptides induced more stable HLA-A*02:01 molecules than the other ligands, although affinities were similar. Remarkably, Tpn(1-87) could functionally discriminate the selected SYFPEITHI peptides from the other peptide binders with high sensitivity and specificity. We suggest that this HLA-I- and peptide-specific function, together with the functions exerted by the more C-terminal parts of tapasin, are major features of tapasin-mediated HLA-I quality control. These findings are important for understanding the biogenesis of HLA-I molecules, the selection of presented T-cell epitopes, and the identification of immunogenic targets in both basic research and vaccine design.     

Functional characterization of CTL against gp100 altered peptide ligands

In this study, four modified gp100 peptides were designed by combining amino acids from the melanoma peptide antigen gp100((209-217)) with preferred primary and auxiliary HLA-A *0201 anchor residues previously identified from combinatorial peptide library screening with recombinant HLA-A*0201. These modified peptides demonstrated stronger binding affinity for the HLA-A*0201 molecule compared to wild-type gp100 peptide. Nine CTL lines generated from patients immunized with the g209-2 M peptide and one CTL line from a non-immunized patient were tested for the ability to respond to these modified gp100 peptides. Stimulation of CTL by two of four modified peptides induced higher levels of IFN-gamma secretion than the wild-type gp100 peptide, demonstrating that higher peptide binding affinity for HLA molecules does not necessarily equate to functional activity of CTL. Two major and one minor CTL recognition pattern were observed, irrespective of previous peptide immunization, suggesting that multiple, rationally designed modified tumor peptides for the same epitope stimulate a broad CTL response by activating multiple CTL capable of cross-reacting with the natural antigenic peptide.     

An HLA-A3-binding prostate acid phosphatase-derived peptide can induce CTLs restricted to HLA-A2 and -A24 alleles

We previously reported peptide vaccine candidates for HLA-A3 supertype (-A3, -A11, -A31, -A33)-positive cancer patients. In the present study, we examined whether those peptides can also induce cytotoxic T lymphocyte (CTL) activity restricted to HLA-A2, HLA-A24, and HLA-A26 alleles. Fourteen peptides were screened for their binding activity to HLA-A*0201, -A*0206, -A*0207, -A*2402, and -A*2601 molecules and then tested for their ability to induce CTL activity in peripheral blood mononuclear cells (PBMCs) from prostate cancer patients. Among these peptides, one from the prostate acid phosphatase protein exhibited binding activity to HLA-A*0201, -A*0206, and -A*2402 molecules. In addition, PBMCs stimulated with this peptide showed that HLA-A2 or HLA-A24 restricted CTL activity. Their cytotoxicity toward cancer cells was ascribed to peptide-specific and CD8⁺ T cells. These results suggest that this peptide could be widely applicable as a peptide vaccine for HLA-A3 supertype-, HLA-A2-, and -A24-positive cancer patients.     

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).     

Methods for prediction of peptide binding to MHC molecules: a comparative study

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.     

ProPred1: prediction of promiscuous MHC Class-I binding sites

SUMMARY: ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. AVAILABILITY: Server is available at http://www.imtech.res.in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/ Supplementary information: Matrices and document on server are available at http://www.imtech.res.in/raghava/propred1/page2.html     

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.     

Structural prediction of peptides binding to MHC class I molecules

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.     

Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201.

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q2 = 0.5724 and a high rpred2 = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.     

HLA-A3 supermotif defined by quantitative structure-activity relationship analysis

Activation of a cytotoxic T cell requires specific binding of antigenic peptides to major histocompatibility complex (MHC) molecules. This paper reports a study of peptides binding to members of the HLA-A3 superfamily using a recently developed 2D-QSAR method, called the additive method. Four alleles with high phenotype frequency were included in the study: A*0301, A*1101, A*3101 and A*6801. The influence of each of the 20 amino acids at each position of the peptide on binding was studied. A refined A3 supertype motif was defined in the study.     

Prediction of HLA-DQ3.2beta ligands: evidence of multiple registers in class II binding peptides

MOTIVATION: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. METHODS: We describe a structure-based prediction model for modeling peptide-DQ3.2beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2beta binding and non-binding peptides obtained from biochemical and functional studies. RESULTS: Our model predicts DQ3.2beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.     

T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research

COVID-19 caused by SARS-CoV-2 is pandemic with a severe morbidity and mortality rate across the world. Despite the race for effective vaccine and drug against further expansion and fatality rate of this novel coronavirus, there is still lack of effective antiviral therapy. To this effect, we deemed it necessary to identify potential B and T cell epitopes from the envelope S protein. This can be used as potential targets to develop anti-SARS-CoV-2 vaccine preparations. In this study, we used immunoinformatics to identify conservative B and T cell epitopes for S proteins of SARS-CoV-2, which might play roles in the initiation of SARS-CoV-2 infection. We identified the B cell and T cell peptide epitopes of S protein and their antigenicity, as well as the interaction between the peptide epitopes and human leucocyte antigen (HLA). Among the B cell epitopes, ‘EILDITPCSFGGVS’ has the highest score of antigenicity and great immunogenicity. In T cell epitopes, MHC-I peptide ‘KIADYNYKL’ and MHC-II peptide ‘LEILDITPC’ were identified as high antigens. Besides, docking analysis showed that the predicted peptide ‘KIADYNYKL’ was closely bound to the HLA-A*0201. The results of molecular dynamics simulation through GROMACS software showed that ‘HLA-A*0201~peptide’ complex was very stable. And the peptide we selected could induce the T cell response similar to that of SARS-CoV-2 infection. Moreover, the predicted peptides were highly conserved in different isolates from different countries. The antigenic epitopes presumed in this study were effective new vaccine targets to prevent SARS-CoV-2 infection.