Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein’s potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response–one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.     

Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) belongs to the coronaviridae family. In spite of several outbreaks in the very recent years, no vaccine against this deadly virus is developed yet. In this study, the receptor binding domain (RBD) of Spike (S) glycoprotein of MERS-CoV was analyzed through Computational Immunology approach to identify the antigenic determinants (epitopes). In order to do so, the sequences of S glycoprotein that belong to different geographical regions were aligned to observe the conservancy of MERS-CoV RBD. The immune parameters of this region were determined using different in silico tools and Immune Epitope Database (IEDB). Molecular docking study was also employed to check the affinity of the potential epitope towards the binding cleft of the specific HLA allele. The N-terminus RBD (S367-S606) of S glycoprotein was found to be conserved among all the available strains of MERS-CoV. Based on the lower IC₅₀ value, a total of eight potential T-cell epitopes and 19 major histocompatibility complex (MHC) class-I alleles were identified for this conserved region. A 9-mer epitope CYSSLILDY displayed interactions with the maximum number of MHC class-I molecules and projected the highest peak in the B-cell antigenicity plot which concludes that it could be a better choice for designing an epitope based peptide vaccine against MERSCoV considering that it must undergo further in vitro and in vivo experiments. Moreover, in molecular docking study, this epitope was found to have a significant binding affinity of -8.5 kcal/mol towards the binding cleft of the HLA-C*12:03 molecule.     

Peptide-MHC Cellular Microarray with Innovative Data Analysis System for Simultaneously Detecting Multiple CD4 T-Cell Responses

Background

Peptide:MHC cellular microarrays have been proposed to simultaneously characterize multiple Ag-specific populations of T cells. The practice of studying immune responses to complicated pathogens with this tool demands extensive knowledge of T cell epitopes and the availability of peptide:MHC complexes for array fabrication as well as a specialized data analysis approach for result interpretation.

Methodology/Principal Findings

We co-immobilized peptide:DR0401 complexes, anti-CD28, anti-CD11a and cytokine capture antibodies on the surface of chamber slides to generate a functional array that was able to detect rare Ag-specific T cell populations from previously primed in vitro T cell cultures. A novel statistical methodology was also developed to facilitate batch processing of raw array-like data into standardized endpoint scores, which linearly correlated with total Ag-specific T cell inputs. Applying these methods to analyze Influenza A viral antigen-specific T cell responses, we not only revealed the most prominent viral epitopes, but also demonstrated the heterogeneity of anti-viral cellular responses in healthy individuals. Applying these methods to examine the insulin producing beta-cell autoantigen specific T cell responses, we observed little difference between autoimmune diabetic patients and healthy individuals, suggesting a more subtle association between diabetes status and peripheral autoreactive T cells.

Conclusions/Significance

The data analysis system is reliable for T cell specificity and functional testing. Peptide:MHC cellular microarrays can be used to obtain multi-parametric results using limited blood samples in a variety of translational settings.     

Heat shock protein-mediated cross-presentation of exogenous HIV antigen on HLA class I and class II

Strong CD4(+) and CD8(+) T cell responses are considered important immune components for controlling HIV infection, and their priming may be central to an effective HIV vaccine. We describe in this study an approach by which multiple CD4(+) and CD8(+) T cell epitopes are processed and presented from an exogenously added HIV-1 Gag-p24 peptide of 32 aa complexed to heat shock protein (HSP) gp96. CD8(+) T cell recognition of the HSP/peptide complex, but not the peptide alone, was inhibited by brefeldin A, suggesting an endoplasmic reticulum-dependent pathway. This is the first report to describe efficient processing and simultaneous presentation of overlapping class I- and class II-restricted epitopes from the same extracellularly added precursor peptide complexed to HSP. Given previous reports of the strong immunogenicity of HSP/peptide complexes, the present data suggest that HSP-complexed peptides containing multiple MHC class I- and class II-restricted epitopes represent potential vaccine candidates for HIV and other viral infections suitable to induce effective CTL memory by simultaneously providing CD4 T cell help.     

Dynamical characterization of two differentially disease associated MHC class I proteins in complex with viral and self-peptides

Major histocompatibility complex (MHC) class I proteins are expressed on the cell surface where they present foreign and self-peptides to effector cells of the immune system. While an understanding of the structural prerequisites for antigen presentation has already been achieved, insight into subtype- or peptide-dependent dynamical characteristics of a peptide–MHC antigen is so far largely obscure. We approached this problem by employing 400-ns molecular dynamics simulations with two human MHC class I subtypes as model systems: the ankylosing spondylitis-associated HLA-B127:05 and the non-ankylosing spondylitis-associated HLA-B127:09. Both proteins differ only by a micropolymorphism at the floor of the peptide binding groove (Asp116His). A viral (pLMP2) and three self-peptides (pVIPR, pGR, and TIS) were evaluated. The stability of the binding grooves was found to be both subtype dependent and peptide dependent. A detachment from the C- and/or N-terminal pockets was observed for all peptides except TIS, resulting in a stabilization of the α1-helix in both TIS-displaying subtypes. Estimates of the entropy associated with the bound peptides showed an increased entropy for pLMP2 presented by B127:05 as compared to B127:09, in contrast to the self-peptides. Additionally, the flexibility of the α1-helix that is probably important for receptor binding to the B27:peptide epitope is significantly enhanced for B127:05. These in silico results show that the dynamic properties of peptide–MHC complexes are affected both by the bound peptide and by micropolymorphisms of the heavy chain. Our findings suggest a role for the conformational flexibility of MHC class I molecules in the context of recognition by receptors on effector cells.     

Proteome‐wide B and T cell epitope repertoires in outer membrane proteins of Mycobacterium avium subsp. paratuberculosis have vaccine and diagnostic relevance: a holistic approach

Mycobacterium avium subsp. paratuberculosis (MAP) is an etiological agent of chronic inflammation of the intestine among ruminants and humans. Currently, there are no effective vaccines and sensitive diagnostic tests available for its control and detection. For this, it is of paramount importance to identify the MAP antigens, which may be immunologically recognized by the host immune system. To address this challenge, we performed identification of the immunogenic epitopes in the MAP outer membrane proteins (OMPs). We have previously identified 57 MAP proteins as OMPs [Rana A, Rub A, Akhter Y. 2014. Molecular BioSystems, 10:2329–2337] and have evaluated them for the epitope selection and analysis employing a computational approach. Thirty‐five MAP OMPs are reported with nine‐mer peptides showing high binding affinity to major histocompatibility complex (MHC) class I molecules and 28 MAP OMPs with 15‐mer peptides of high binding affinity for MHC class II molecules. The presence of MHC binding epitopes indicates the potential cell‐mediated immune response inducing capacity of these MAP OMPs in infected host. To further investigate the humoral response inducing properties of OMPs of MAP, we report potential B cell epitopes based on the sequences of peptide antigens and their molecular structures. We also report 10 proteins having epitopes for both B and T cells representing potential candidates which may invoke both humoral and cellular immune responses in the host. These findings will greatly accelerate and expedite the formulation of effective and cost‐efficient vaccines and diagnostic tests against MAP infection. Copyright © 2015 John Wiley & Sons, Ltd.     

Computational and Experimental Validation of B and T-Cell Epitopes of the In Vivo Immune Response to a Novel Malarial Antigen

Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design.     

Identification and validation of T-cell epitopes in outer membrane protein (OMP) of Salmonella typhi

This study aims to design epitope-based peptides for the utility of vaccine development by targeting outer membrane protein F (Omp F), because two available licensed vaccines, live oral Ty21a and injectable polysaccharide, are 50% to 80% protective with a higher rate of side effects. Conventional vaccines take longer time for development and have less differentiation power between vaccinated and infected cells. On the other hand, Peptide-based vaccines present few advantages over other vaccines, such as stability of peptide, ease to manufacture, better storage, avoidance of infectious agents during manufacture, and different molecules can be linked with peptides to enhance their immunogenicity. Omp F is highly conserved and facilitates attachment and fusion of Salmonella typhi with host cells. Using various databases and tools, immune parameters of conserved sequences from Omp F of different isolates of Salmonella typhi were tested to predict probable epitopes. Binding analysis of the peptides with MHC molecules, epitopes conservancy, population coverage, and linear B cell epitope prediction were analyzed. Among all those predicted peptides, ESYTDMAPY epitope interacted with six MHC alleles and it shows highest amount of interaction compared to others. The cumulative population coverage for these epitopes as vaccine candidates was approximately 70%. Structural analysis suggested that epitope ESYTDMAPY fitted well into the epitope-binding groove of HLA-C*12:03, as this HLA molecule was common which interact with each and every predicted epitopes. So, this potential epitope may be linked with other molecules to enhance its immunogenicity and used for vaccine development.     

In silico analysis and modeling of ACP-MIP–PilQ chimeric antigen from Neisseria meningitidis serogroup B

Background:

Neisseria meningitidis, a life-threatening human pathogen with the potential to cause large epidemics, can be isolated from the nasopharynx of 5–15% of adults. The aim of the current study was to evaluate biophysical and biochemical properties and immunological aspects of chimeric acyl-carrier protein-macrophage infectivity potentiator protein-type IV pilus biogenesis protein antigen (ACP-MIP-PilQ) from N. meningitidis serogroup B strain.

Methods:

Biochemical properties and multiple alignments were predicted by appropriate web servers. Secondary molecular structures were predicted based on Chou and Fasman, Garnier-Osguthorpe-Robson, and Neural Network methods. Tertiary modeling elucidated conformational properties of the chimeric protein. Proteasome cleavage and transporter associated with antigen processing (TAP) binding sites, and T- and B-cell antigenic epitopes, were predicted using bioinformatic web servers.

Results:

Based on our in silico and immunoinformatics analyses, the ACP-MIP-PilQ protein (AMP) can induce high-level cross-strain bactericidal activity. In addition, several immune proteasomal cleavage sites were detected. The 22 epitopes associated with MHC class I and class II (DR) alleles were confirmed in the AMP. Thirty linear B-cell epitopes as antigenic regions were predicted from the full-length protein.

Conclusion:

All predicted properties of the AMP indicate it could be a good candidate for further immunological in vitro and in vivo studies.Key Words: Chimeric protein, In silico, Neisseria meningitides, serogroup B, Vaccine     

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).     

Differential selection pressure exerted on HIV by CTL targeting identical epitopes but restricted by distinct HLA alleles from the same HLA supertype

HLA diversity is seen as a major challenge to CTL vaccines against HIV. One current approach focuses on "promiscuous" epitopes, presented by multiple HLA alleles from within the same HLA supertype. However, the effectiveness of such supertype vaccines depends upon the functional equivalence of CTL targeting a particular epitope, irrespective of the restricting HLA. In this study, we describe the promiscuous HIV-specific CTL epitopes presented by alleles within the B7 supertype. Substantial differences were observed in the ability of CTL to select for escape mutation when targeting the same epitope but restricted by different HLA. This observation was common to all six promiscuous B7 epitopes identified. Moreover, with one exception, there were no significant differences in the frequency, magnitude, or immunodominance of the CTL responses restricted by different HLA alleles to explain these discrepancies. This suggests that the unique peptide/MHC complexes generated by even closely related HLA induce CTL responses that are qualitatively different. This hypothesis is supported by additional differences observed between CTL targeting identical epitopes but restricted by different HLA: first, the occurrence of distinct, HLA-specific escape mutation; second, the recruitment of distinct TCR repertoires by particular peptide/MHC complexes; and, third, significant differences in the functional avidity of CTL. Taken together, these data indicate that significant functional differences exist between CTL targeting identical epitopes but restricted by different, albeit closely related HLA. These findings are of relevance to vaccine approaches that seek to exploit HLA supertypes to overcome the problem of HLA diversity.     

SVRMHC prediction server for MHC-binding peptides

Background  

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.     

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.     

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated (‘attenuated’) forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.     

Design and use of conditional MHC class I ligands

Major histocompatibility complex (MHC) class I molecules associate with a variety of peptide ligands during biosynthesis and present these ligands on the cell surface for recognition by cytotoxic T cells. We have designed conditional MHC ligands that form stable complexes with MHC molecules but degrade on command, by exposure to a defined photostimulus. 'Empty MHC molecules' generated in this manner can be loaded with arrays of peptide ligands to determine MHC binding properties and to monitor antigen-specific T-cell responses in a high-throughput manner. We document the value of this approach by identifying cytotoxic T-cell epitopes within the H5N1 influenza A/Vietnam/1194/04 genome.     

Candidate epitope identification using peptide property models: application to cancer immunotherapy

Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on peptide property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.     

Viral peptide immunogens: current challenges and opportunities.

Synthetic peptide vaccines have potential to control viral infections. Successful experimental models using this approach include the protection of mice against the lethal Sendai virus infection by MHC class I binding CTL peptide epitope. The main benefit of vaccination with peptide epitopes is the ability to minimize the amount and complexity of a well-defined antigen. An appropriate peptide immunogen would also decrease the chance of stimulating a response against self-antigens, thereby providing a safer vaccine by avoiding autoimmunity. In general, the peptide vaccine strategy needs to dissect the specificity of antigen processing, the presence of B-and T-cell epitopes and the MHC restriction of the T-cell responses. This article briefly reviews the implications in the design of peptide vaccines and discusses the various approaches that are applied to improve their immunogenicity.     

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.     

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     

Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles

Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.