MHCPred: A server for quantitative prediction of peptide-MHC binding

Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-based multivariate statistical approach to the quantitative prediction of peptide binding to major histocom- patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401 and HLA-DRB*0701). MHCPred is available from the URL: http://www.jenner.ac.uk/MHCPred.     

Quantitative approaches to computational vaccinology

This article reviews the newly released JenPep database and two new powerful techniques for T-cell epitope prediction: (i) the additive method; and (ii) a 3D-Quantitative Structure Activity Relationships (3D-QSAR) method, based on Comparative Molecular Similarity Indices Analysis (CoMSIA). The JenPep database is a family of relational databases supporting the growing need of immunoinformaticians for quantitative data on peptide binding to major histocompatibility complexes and to the Transporters associated with Antigen Processing (TAP). It also contains an annotated list of T-cell epitopes. The database is available free via the Internet (http://www.jenner.ac.uk/JenPep). The additive prediction method is based on the assumption that the binding affinity of a peptide depends on the contributions from each amino acid as well as on the interactions between the adjacent and every second side-chain. In the 3D-QSAR approach, the influence of five physicochemical properties (steric bulk, electrostatic potential, local hydrophobicity, hydrogen-bond donor and hydrogen-bond acceptor abilities) on the affinity of peptides binding to MHC molecules were considered. Both methods were exemplified through their application to the well-studied problem of peptides binding to the human class I MHC molecule HLA-A*0201.     

MHCPred 2.0

The accurate computational prediction of T-cell epitopes can greatly reduce the experimental overhead implicit in candidate epitope identification within genomic sequences. In this article we present MHCPred 2.0, an enhanced version of our online, quantitative T-cell epitope prediction server. The previous version of MHCPred included mostly alleles from the human leukocyte antigen A (HLA-A) locus. In MHCPred 2.0, mouse models are added and computational constraints removed. Currently the server includes 11 human HLA class I, three human HLA class II, and three mouse class I models. Additionally, a binding model for the human transporter associated with antigen processing (TAP) is incorporated into the new MHCPred. A tool for the design of heteroclitic peptides is also included within the server. To refine the veracity of binding affinities prediction, a confidence percentage is also now calculated for each peptide predicted. AVAILABILITY: As previously, MHCPred 2.0 is freely available at the URL http://www.jenner.ac.uk/MHCPred/ CONTACT: Darren R. Flower (darren.flower@jenner.ac.uk).     

JenPep: a database of quantitative functional peptide data for immunology

MOTIVATION: The compilation of quantitative binding data underlies attempts to derive tools for the accurate prediction of epitopes in cellular immunology and is part of our concerted goal to develop practical computational vaccinology. RESULTS: JenPep is a family of relational databases supporting the growing community of immunoinformaticians. It contains quantitative data on peptide binding to Major Histocompatibility Complexes (MHCs) and to Transmembrane Peptide Transporter (TAP), as well as an annotated list of T-cell epitopes. AVAILABILITY: The database is available via the Internet. An HTML interface allowing searching of the database can be found at the following address: http://www.jenner.ac.uk/JenPep.     

T cell epitopes of human myelin oligodendrocyte glycoprotein identified in HLA-DR4 (DRB1*0401) transgenic mice are encephalitogenic and are presented by human B cells.

Myelin oligodendrocyte glycoprotein (MOG) is an Ag present in the myelin sheath of the CNS thought to be targeted by the autoimmune T cell response in multiple sclerosis (MS). In this study, we have for the first time characterized the T cell epitopes of human MOG restricted by HLA-DR4 (DRB1*0401), an MHC class II allele associated with MS in a subpopulation of patients. Using MHC binding algorithms, we have predicted MOG peptide binding to HLA-DR4 (DRB1*0401) and subsequently defined the in vivo T cell reactivity to overlapping MOG peptides by testing HLA-DR4 (DRB1*0401) transgenic mice immunized with recombinant human (rh)MOG. The data indicated that MOG peptide 97-108 (core 99-107, FFRDHSYQE) was the immunodominant HLA-DR4-restricted T cell epitope in vivo. This peptide has a high in vitro binding affinity for HLA-DR4 (DRB1*0401) and upon immunization induced severe experimental autoimmune encephalomyelitis in the HLA-DR4 transgenic mice. Interestingly, the same peptide was presented by human B cells expressing HLA-DR4 (DRB1*0401), suggesting a role for the identified MOG epitopes in the pathogenesis of human MS.     

Tetramer-guided epitope mapping: rapid identification and characterization of immunodominant CD4+ T cell epitopes from complex antigens

T cell responses to Ags involve recognition of selected peptide epitopes contained within the antigenic protein. In this report, we describe a new approach for direct identification of CD4+ T cell epitopes of complex Ags that uses human class II tetramers to identify reactive cells. With a panel of 60 overlapping peptides covering the entire sequence of the VP16 protein, a major Ag for HSV-2, we generated a panel of class II MHC tetramers loaded with peptide pools that were used to stain peripheral lymphocytes of an HSV-2 infected individual. With this approach, we identified four new DRA1*0101/DRB1*0401- and two DRA1*0101/DRB1*0404-restricted, VP16-specific epitopes. By using tetramers to sort individual cells, we easily obtained a large number of clones specific to these epitopes. Although DRA1*0101/DRB1*0401 and DRA1*0101/DRB1*0404 are structurally very similar, nonoverlapping VP16 epitopes were identified, illustrating high selectivity of individual allele polymorphisms within common MHC variants. This rapid approach to detecting CD4+ T cell epitopes from complex Ags can be applied to any known Ag that gives a T cell response.     

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates: E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 < 50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.     

Identification of peptides from foot‐and‐mouth disease virus structural proteins bound by class I swine leukocyte antigen (SLA) alleles,SLA‐1*0401 and SLA‐2*0401

Characterization of the peptide‐binding specificity of swine leukocyte antigen (SLA) class I and II molecules is critical to the understanding of adaptive immune responses of swine toward infectious pathogens. Here, we describe the complete binding motif of the SLA‐2*0401 molecule based on a positional scanning combinatorial peptide library approach. By combining this binding motif with data achieved by applying the NetMHCpan peptide prediction algorithm to both SLA‐1*0401 and SLA‐2*0401, we identified high‐affinity binding peptides. A total of 727 different 9mer and 726 different 10mer peptides within the structural proteins of foot‐and‐mouth disease virus (FMDV), strain A24 were analyzed as candidate T‐cell epitopes. Peptides predicted by the NetMHCpan were tested in ELISA for binding to the SLA‐1*0401 and SLA‐2*0401 major histocompatibility complex class I proteins. Four of the 10 predicted FMDV peptides bound to SLA‐2*0401, whereas five of the nine predicted FMDV peptides bound to SLA‐1*0401. These methods provide the characterization of T‐cell epitopes in response to pathogens in more detail. The development of such approaches to analyze vaccine performance will contribute to a more accelerated improvement of livestock vaccines by virtue of identifying and focusing analysis on bona fide T‐cell epitopes.     

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     

BPROMPT: A consensus server for membrane protein prediction

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.     

An empirical method for the prediction of T-cell epitopes

Identification of T-cell epitopes from foreign proteins is the current focus of much research. Methods using simple two or three position motifs have proved useful in epitope prediction for major histocompatibility complex (MHC) class I, but to date not for MHC class II molecules. We utilized data from pool sequence analysis of peptides eluted from two HLA-DR13 alleles to construct a computer algorithm for predicting the probability that a given sequence will be naturally processed and presented on these alleles. We assessed the ability of this method to predict know self-peptides from these DR-13 alleles, DRB1 ^*1301 and *1302, as well as an immunodominant T-cell epitope. We also compared the predictions of this scoring procedure with the measured binding affinities of a panel of overlapping peptides from hepatitis B virus surface antigen. We concluded that this method may have wide application for the prediction of T-cell epitopes for both MHC class I and class II molecules.     

Immunoinformatics Approach to Design T-cell Epitope-Based Vaccine Against Hendra Virus

Screening of HLA class II epitope-based peptides as potential vaccine candidates is one of the most rational approach for vaccine development against Hendra virus (HeV) infection, for which currently there is no successful vaccine in practice. In this study, screening of epitopes from HeV proteins viz matrix, glycoprotein, nucleocapsid, fusion, C protein, V protein, W protein and polymerase, followed by highest binding affinity & molecular dynamic simulation of selected T-cell epitopes with their corresponding HLA class II alleles has been done. The server ProPred facilitates the binding prediction of HLA class II allele specific epitopes from the antigenic protein sequences of HeV. PEPstrMOD server was used for PDB structure modeling of the screened epitopes and MODELLER was used for HLA alleles modeling. We docked the selected T-cell epitopes with their corresponding HLA allele structures using the AutoDock 4.2 tool. Further the selected docked complex structures were optimized by NAnoscale Molecular Dynamics program (NAMD) at 5 ps, with the CHARMM-22 force field parameter incorporated in Visual Molecular Dynamics (VMD 1.9.2) and complex structure stability was evaluated by calculating RMSD values. Epitopes IRIFVPATN (Nucleocapsid), MRNLLSQSL (Nucleocapsid), VRRAGKYYS (Matrix) and VRLKCLLCG (Fusion) proteins have shown considerable binding with DRB1*0806, DRB1*1304, DRB1*0701 and DRB1*0301 HLA class II allele respectively. Toxicity, antigenicity and population coverage of epitopes IRIFVPATN, MRNLLSQSL, VRRAGKYYS and VRLKCLLCG were analyzed by Toxin Pred, Vexijen and IEDB tool, respectively. The potential T-cell epitopes can be utilized in designing comprehensive epitope-based vaccines and diagnostic kits against Hendra virus after further in-vivo studies.

     

BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity

Effective identification of major histocompatibility complex (MHC) molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class Ⅱ MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler, ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC (http:∥biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request). Using a standard measure of AUC (Area Under the receiver operating characteristic Curves), we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class Ⅱ MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.     

A novel strategy of epitope design in Neisseria gonorrhoeae

In spite of genome sequences of both human and N. gonorrhoeae in hand, vaccine for gonorrhea is yet not available. Due to availability of several host and pathogen genomes and numerous tools for in silico prediction of effective B-cell and T-cell epitopes; recent trend of vaccine designing has been shifted to peptide or epitope based vaccines that are more specific, safe, and easy to produce. In order to design and develop such a peptide vaccine against the pathogen, we adopted a novel computational approache based on sequence, structure, QSAR, and simulation methods along with fold level analysis to predict potential antigenic B-cell epitope derived T-cell epitopes from four vaccine targets of N. gonorrhoeae previously identified by us [Barh and Kumar (2009) In Silico Biology 9, 1-7]. Four epitopes, one from each protein, have been designed in such a way that each epitope is highly likely to bind maximum number of HLA molecules (comprising of both the MHC-I and II) and interacts with most frequent HLA alleles (A*0201, A*0204, B*2705, DRB1*0101, and DRB1*0401) in human population. Therefore our selected epitopes are highly potential to induce both the B-cell and T-cell mediated immune responses. Of course, these selected epitopes require further experimental validation.     

Computer-based comparison of structural features of envelope protein of Alkhurma hemorrhagic fever virus with the homologous proteins of two closest viruses

The aim of this study was prediction of epitopes and medically important structural properties of protein E of Alkhurma hemorrhagic fever virus (AHFV) and comparing these features with two closely relates viruses, i.e. Kyasanur Forest disease virus (KFDV) and Tick-borne encephalitis virus (TBEV) by bioinformatics tools. Prediction of evolutionary distance, localization, sequence of signal peptides, C, N O glycosylation sites, transmembrane helices (TMHs), cysteine bond positions and B cell and T cell epitopes of E proteins were performed. 2D-MH, Virus-PLoc, Signal-CF, EnsembleGly, MemBrain, DiANNA, BCPREDS and MHCPred servers were applied for the prediction. According to the results, the evolutionary distance of E protein of AHFV and two other viruses was almost equal. In all three proteins of study, residues 1-35 were predicted as signal sequences and one asparagine was predicted to be glycosylated. Results of prediction of transmembrane helices showed one TMH at position 444-467 and the other one at position 476-490. Twelve cysteines were potentially involved to form six disulfide bridges in the proteins. Four parts were predicted as B cell epitopes in E protein of AHFV. One epitope was conserved between three proteins of study. The only conserved major histocompatibility complex (MHC) binding epitope between three viruses was for DRB0401 allele. As there are not much experimental data available about AHFV, computer-aided study and comparison of E protein of this virus with two closely related flaviviruses can help in better understanding of medical properties of the virus.     

Selection of T-cell epitopes from foot-and-mouth disease virus reflects the binding affinity to different cattle MHC class II molecules

The major histocompatibility complex (MHC)-restricted selection of T-cell epitopes of foot-and-mouth disease virus (FMDV) by individual cattle MHC class II DR (BoLA-DR) molecules was studied in a direct MHC-peptide binding assay. By in vitro priming of T lymphocytes derived from animals homozygous for both MHC class I and II, five T-cell epitopes were analyzed in the context of three MHC class II haplotypes. We found that the presentation of these T-cell epitopes was mediated by DR molecules, since blocking this pathway of antigen presentation using monoclonal antibody TH14B completely abolished the proliferative responses against the peptides. To study the DR-restricted presentation of these T-cell epitopes, a direct MHC-peptide binding assay on isolated cattle DR molecules was developed. Purified cattle MHC class II DR molecules of the BoLA-DRB3*0201, BoLA-DRB3*1101, and BoLA-DRB3*1201 alleles were isolated from peripheral blood mononuclear cells. For each allele, one of the identified T-cell epitopes was biotinylated, and used as a marker peptide for the development of a competitive MHC-peptide binding assay. Subsequently, the T-cell epitopes of FMDV with functionally defined MHC class II specificity were analyzed in this binding assay. The affinity of the epitopes to bind to certain DR molecules was significantly correlated to the capacity to induce T-cell proliferation. This demonstrated at the molecular level that the selection of individual T-cell epitopes found at the functional level was indeed the result of MHC restriction.     

Transporter associated with antigen processing preselection of peptides binding to the MHC: a bioinformatic evaluation

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.     

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.     

HLA-DR restricted peptide candidates for bee venom immunotherapy

T cell epitopes containing peptides have been recently proposed as an alternative to conventional immunotherapy of allergic diseases because they are expected to be better tolerated than allergen extracts. A principal limitation to their clinical use is that they present an important diversity, which primarily results from the polymorphism of HLA class II molecules. In Caucasian populations, however, seven alleles of the most expressed molecules (namely DRB1*0101, DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1301, and DRB1*1501) predominate. Peptides from allergens that would efficiently bind to them should be potential candidates for specific immunotherapy. In this paper, we have determined the peptides present in the major bee venom allergen by investigating the capacity of synthetic peptides that encompass its whole sequence to bind to each allele. Several efficient binders have been identified and are either allele-specific or common to several HLA-DR molecules. Interestingly enough, the 81-97 sequence is universal in the sense that it binds to all studied molecules. This sequence is surrounded by several active regions, which make the 76-106 sequence particularly rich of binding determinants and a good candidate for specific immunotherapy. Statistical analyses of the binding data also provide an overview of the preponderant HLA-DR alleles specificity.     

Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.