Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.     

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).     

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.     

Computational characterization of epitopic region within the outer membrane protein candidate in Flavobacterium columnare for vaccine development

Abstract

Gram-negative bacteria is the main causative agents for columnaris disease outbreak to finfishes. The outer membrane proteins (OMPs) candidate of Flavobacterium columnare bacterial cell served a critical component for cellular invasion targeted to the eukaryotic cell and survival inside the macrophages. Therefore, OMPs considered as the supreme element for the development of promising vaccine against F. columnare. Implies advanced in silico approaches, the predicted 3-D model of targeted OMPs were characterized by the Swiss model server and validated through Procheck programs and Protein Structure Analysis (ProSA) web server. The protein sequences having B-cell binding sites were preferred from sequence alignment; afterwards the B cell epitopes prediction was prepared using the BCPred and amino acid pairs (AAP) prediction algorithms modules of BCPreds. Consequently, the selected antigenic amino acids sequences (B-cell epitopic regions) were analyzed for T-cell epitopes determination (MHC I and MHC II alleles binding sequence) performing the ProPred 1 and ProPred server respectively. The epitopes (9 mer: IKKYEPAPV, YGPNYKWKF and YRGLNVGTS) within the OMPs binds to both of the MHC classes (MHC I and MHC II) and covered highest number of MHC alleles are characterized. OMPs of F. columnare being conserved across serotypes and highly immunogenic for their exposed epitopes on the cell surface as a potent candidate focus to vaccine development for combating the disease problems in commercial aquaculture. The portrayed epitopes might be beneficial for practical designing of abundant peptide-based vaccine development against the columnaris through boosting up the advantageous immune responses.

Communicated by Ramaswamy H. Sarma     

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.     

Prediction of MHC class II binding affinity using SMM-align,a novel stabilization matrix alignment method

Background  

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles.     

Application of support vector machines for T-cell epitopes prediction

MOTIVATION: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development. RESULTS: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding. SUPPLEMENTARY INFORMATION: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf     

Mass spectrometry analysis and quantitation of peptides presented on the MHC II molecules of mouse spleen dendritic cells

Major histocompatibility complex class II (MHC II) molecules are expressed on the surface of antigen-presenting cells and display short bound peptide fragments derived from self- and nonself antigens. These peptide-MHC complexes function to maintain immunological tolerance in the case of self-antigens and initiate the CD4(+) T cell response in the case of foreign proteins. Here we report the application of LC-MS/MS analysis to identify MHC II peptides derived from endogenous proteins expressed in freshly isolated murine splenic DCs. The cell number was enriched in vivo upon treatment with Flt3L-B16 melanoma cells. In a typical experiment, starting with about 5 × 10(8) splenic DCs, we were able to reliably identify a repertoire of over 100 MHC II peptides originating from about 55 proteins localized in membrane (23%), intracellular (26%), endolysosomal (12%), nuclear (14%), and extracellular (25%) compartments. Using synthetic isotopically labeled peptides corresponding to the sequences of representative bound MHC II peptides, we quantified by LC-MS relative peptide abundance. In a single experiment, peptides were detected in a wide concentration range spanning from 2.5 fmol/μL to 12 pmol/μL or from approximately 13 to 2 × 10(5) copies per DC. These peptides were found in similar amounts on B cells where we detected about 80 peptides originating from 55 proteins distributed homogenously within the same cellular compartments as in DCs. About 90 different binding motifs predicted by the epitope prediction algorithm were found within the sequences of the identified MHC II peptides. These results set a foundation for future studies to quantitatively investigate the MHC II repertoire on DCs generated under different immunization conditions.     

In silico analysis for development of epitopes-based peptide vaccine against Alkhurma hemorrhagic fever virus

Abstract

Alkhurma hemorrhagic fever virus (ALKV) causes a fatal clinical disease in human beings of different tropical and sub-tropical regions. Recently, the ALKV epidemics have raised a great public health concern with the room for improvement in the essential therapeutic interventions. Despite increased realistic clinical cases of ALKV infection, the efficient vaccine or immunotherapy is not yet available to-date. Therefore, the current study aimed to analyze the envelope glycoprotein of ALKV for the development of B-cells and T-cells epitope-based peptide vaccine using the computational in silico method. Utilizing various immunoinformatics approaches, a total of 5 B-cells and 25 T-cells (MHC-I?=?17, MHC-II?=?8) epitope-based peptides were predicted in the current study. All predicted peptides had highest antigenicity and immunogenicity scores along with high binding affinity to human leukocyte antigen (HLA) class II alleles. Among 25T-cell epitopes, three peptides were found alike to have affinity to bind both MHC-I and MHC-II alleles. These outcomes suggested that these predicted epitopes could potentially be used in the development of an efficient vaccine against ALKV, which may enable to elicit both humoral and cell-mediated immunity. Although, these predicted peptides could be useful in designing a candidate vaccine for the prevention of ALKV; however, it’s in vitro and in vivo assessments are prerequisite.

Communicated by Ramaswamy H. Sarma     

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

Background  

The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.     

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.     

Helper T Cell Epitope-Mapping Reveals MHC-Peptide Binding Affinities That Correlate with T Helper Cell Responses to Pneumococcal Surface Protein A

Understanding the requirements for protection against pneumococcal carriage and pneumonia will greatly benefit efforts in controlling these diseases. Several proteins and polysaccharide capsule have recently been implicated in the virulence of and protective immunity against Streptococcus pneumonia. Pneumococcal surface protein A (PspA) is highly conserved among S. pneumonia strains, inhibits complement activation, binds lactoferrin, elicits protective systemic immunity against pneumococcal infection, and is necessary for full pneumococcal virulence. Identification of PspA peptides that optimally bind human leukocyte antigen (HLA) would greatly contribute to global vaccine efforts, but this is hindered by the multitude of HLA polymorphisms. Here, we have used an experimental data set of 54 PspA peptides and in silico methods to predict peptide binding to HLA and murine major histocompatibility complex (MHC) class II. We also characterized spleen- and cervical lymph node (CLN)-derived helper T lymphocyte (HTL) cytokine responses to these peptides after S. pneumonia strain EF3030-challenge in mice. Individual, yet overlapping peptides, 15 amino acids in length revealed residues 199 to 246 of PspA (PspA_199–246) consistently caused the greatest IFN-γ, IL-2, IL-5 and proliferation as well as moderate IL-10 and IL-4 responses by ex vivo stimulated splenic and CLN CD4⁺ T cells isolated from S. pneumonia strain EF3030-challeged F₁ (B6×BALB/c) mice. IEDB, RANKPEP, SVMHC, MHCPred, and SYFPEITHI in silico analysis tools revealed peptides in PspA_199–246 also interact with a broad range of HLA-DR, -DQ, and -DP allelles. These data suggest that predicted MHC class II-peptide binding affinities do not always correlate with T helper (Th) cytokine or proliferative responses to PspA peptides, but when used together with in vivo validation can be a useful tool to choose candidate pneumococcal HTL epitopes.     

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.     

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.     

Peptide length-based prediction of peptide-MHC class II binding

MOTIVATION: Algorithms for predicting peptide-MHC class II binding are typically similar, if not identical, to methods for predicting peptide-MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms. RESULTS: A non-linear relationship between peptide length and peptide-MHC class II binding affinity was identified in the data available for several MHC class II alleles. Peptide length was incorporated into existing prediction algorithms using one of several modifications: using regression to pre-process the data, using peptide length as an additional variable within the algorithm, or representing register shifting in longer peptides. For several datasets and at least two algorithms these modifications consistently improved prediction accuracy. AVAILABILITY: http://malthus.micro.med.umich.edu/Bioinformatics     

Viral adaptation to host immune responses occurs in chronic hepatitis B virus (HBV) infection, and adaptation is greatest in HBV e antigen-negative disease

Hepatitis B virus (HBV)-specific T-cell responses are important in the natural history of HBV infection. The number of known HBV-specific T-cell epitopes is limited, and it is not clear whether viral evolution occurs in chronic HBV infection. We aimed to identify novel HBV T-cell epitopes by examining the relationship between HBV sequence variation and the human leukocyte antigen (HLA) type in a large prospective clinic-based cohort of Asian patients with chronic HBV infection recruited in Australia and China (n = 119). High-resolution 4-digit HLA class I and II typing and full-length HBV sequencing were undertaken for treatment-naïve individuals (52% with genotype B, 48% with genotype C, 63% HBV e antigen [HBeAg] positive). Statistically significant associations between HLA types and HBV sequence variation were identified (n = 49) at 41 sites in the HBV genome. Using prediction programs, we determined scores for binding between peptides containing these polymorphisms and associated HLA types. Among the regions that could be tested, HLA binding was predicted for 14/18 (78%). We identified several HLA-associated polymorphisms involving likely known anchor residues that resulted in altered predicted binding scores. Some HLA-associated polymorphisms fell within known T-cell epitopes with matching HLA restriction. Enhanced viral adaptation (defined as the presence of the relevant HLA and the escaped amino acid) was independently associated with HBeAg-negative disease (P = 0.003). Thus, HBV appears to be under immune pressure in chronic HBV infection, particularly in HBeAg-negative disease.     

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.     

Identification of B- and T-cell epitopes on HtrA protein of Orientia tsutsugamushi

Orientia tsutsugamushi, a cause of scrub typhus is emerging as an important pathogen in several parts of the tropics. The control of this infection relies on rapid diagnosis, specific treatment, and prevention through vector control. Development of a vaccine for human use would be very important as a public health measure. Antibody and T-cell response have been found to be important in the protection against scrub typhus. This study was undertaken to predict the peptide vaccine that elicits both B- and T-cell immunity. The outer-membrane protein, 47-kDa high-temperature requirement A was used as the target protein for the identification of protective antigen(s). Using BepiPred2 program, the potential B-cell epitope PNSSWGRYGLKMGLR with high conservation among O. tsutsugamushi and the maximum surface exposed residues was identified. Using IEDB, NetMHCpan, and NetCTL programs, T-cell epitopes MLNELTPEL and VTNGIISSK were identified. These peptides were found to have promiscuous class-I major histocompatibility complex (MHC) binding affinity to MHC supertypes and high proteasomal cleavage, transporter associated with antigen processing prediction, and antigenicity scores. In the I-TASSER generated model, the C-score was −0.69 and the estimated TM-score was 0.63 ± 0.14. The location of the epitope in the 3D model was external. Therefore, an antibody to this outer-membrane protein epitope could opsonize the bacterium for clearance by the reticuloendothelial system. The T-cell epitopes would generate T-helper function. The B-cell epitope(s) identified could be evaluated as antigen(s) in immunodiagnostic assays. This cocktail of three peptides would elicit both B- and T-cell immune response with a suitable adjuvant and serve as a vaccine candidate.     

TEpredict: Software for T-Cell epitope prediction

The program TEpredict was developed for T-cell epitope prediction. The used models for T-cell epitope prediction were constructed by the partial least squares regression method using the data extracted from the IEDB (Immune Epitope Database), the most complete resource of experimental peptide-MHC binding data. TEpredict is also able to predict proteasomal processing of protein antigens and the ability of produced oligopeptides to bind to the transporters associated with antigen processing, to discard the peptides sharing local similarity with human proteins from the set of predicted epitopes, and to estimate the expected population coverage by the selected peptides using the data on HLA allele genotypic frequencies. The main part of the constructed models demonstrated a high prediction sensitivity (0.50–0.80) in combination with a high specificity (0.75–0.99). Comparative testing demonstrated that TEpredict was competitive with or even superior to the programs ProPred1, SVRMHC, SVMHC, and SYFPEITHI. Thus, TEpredict can become an efficient tool for developing polyepitope vaccines, including the vaccines against various human pathogens, such as HIV and the influenza virus. TEpredict and the source code are available at http://tepredict.source- forge.net.