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.

MHCPEP, a database of MHC-binding peptides: update 1997

MHCPEP (http://wehih.wehi.edu.au/mhcpep/) is a curated database comprising over 13 000 peptide sequences known to bind MHC molecules, Entries are compiled from published reports as well as from direct submissions of experimental data, Each entry contains the peptide sequence, its MHC specificity and where available, experimental method, observed activity, binding affinity, source protein and anchor positions, as well as publication references, The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using WWW or FTP.

MHCPEP, a database of MHC-binding peptides: Update 1996

MHCPEP is a curated database comprising over 9000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains the peptide sequence, its MHC specificity and, when available, experimental method, observed activity, binding affinity, source protein, anchor positions and publication references. The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using WWW, FTP or Gopher.

Cytomegalovirus evasion of natural killer cell responses

Natural killer (NK) cells are an important component of the innate cellular immune system. They are particularly important during the early immune responses following virus infection, prior to the induction of cytotoxic T cells (CTL). Unlike CTL, which recognize specific peptides displayed on the surface of cells by class I MHC, NK cells respond to aberrant expression of cell surface molecules, in particular class I MHC, in a non-specific manner. Thus, cells expressing low levels of surface class I MHC are susceptible to recognition by NK cells, with concomitant triggering of cytolytic and cytokine-mediated responses. Many viruses, including the cytomegaloviruses, downregulate cell surface MHC class I: this is likely to provide protection against CTL-mediated clearance of infected cells, but may also render infected cells sensitive to NK-cell attack. This review focuses upon cytomegalovirus-encoded proteins that are believed to promote evasion of NK-cell-mediated immunity. The class I MHC homologues, encoded by all cytomegaloviruses characterised to date, have been implicated as molecular 'decoys', which may mimic the ability of cellular MHC class I to inhibit NK-cell functions. Results from studies in vitro are not uniform, but in general they support the proposal that the class I homologues engage inhibitory receptors from NK cells and other cell types that normally interact with cellular class I. Consistent with this, in vivo studies of murine cytomegalovirus indicate that the class I homologue is required for efficient evasion of NK-cell-mediated clearance. Recently a second murine cytomegalovirus protein, a C-C chemokine homologue, has been implicated as promoting evasion of NK and T-cell-mediated clearance in vivo.

Human and murine cytomegalovirus evasion of cytotoxic T lymphocyte and natural killer cell-mediated immune responses

Herpesviruses, such as human and murine cytomegalovirus, possess an impressive array of genes believed to assist in virus survival against the host immune response. In this review, we cover the rapidly growing area of cytomegalovirus evasion of cellular immunity, specifically cytotoxic T lymphocytes and natural killer cells. The proposed mechanisms of action of viral proteins involved in blocking peptide presentation to CD8(+) T cells, namely, interference with peptide generation, inhibition of peptide assembly with class I MHC and retention/destabilization of class I MHC complexes, are described. In addition, recent evidence implicating the viral class I MHC-like proteins as inhibitors of natural killer cell-mediated clearance is reviewed, (C) 1998 Academic Press.

Computational binding assays of antigenic peptides

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1-5%.

Murine cytomegalovirus homologues of cellular immunomodulatory genes

The study of 'molecular mimicry' or 'genetic piracy', with respect to the utilisation of cellular genes captured and modified during the course of virus evolution, has been an area of increasing research with the expansion in virus genome sequencing. Examples of cellular immunomodulatory genes which have been captured from hosts have been identified in a number of viruses. This review concentrates upon studies of murine cytomegalovirus (MCMV), investigating the functions of viral genes homologous to G protein-coupled receptors, MHC class I and chemokines, The study of recombinant MCMV engineered with specific disruptions of these genes has revealed their significance during virus replication and dissemination within the host, In the case of the latter two classes of genes, evidence suggests they interfere with cellular immune responses, although the detailed mechanisms underlying this interference have yet to be delineated. Copyright (C) 2000 S. Karger AG, Basel.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Relationship between peptide selectivities of human transporters associated with antigen processing and HLA class I molecules

Efficiency of presentation of a peptide epitope by a MHC class I molecule depends on two parameters: its binding to the MHC molecule and its generation by intracellular Ag processing. In contrast to the former parameter, the mechanisms underlying peptide selection in Ag processing are poorly understood. Peptide translocation by the TAP transporter is required for presentation of most epitopes and may modulate peptide supply to MHC class I molecules. To study the role of human TAP for peptide presentation by individual HLA class I molecules, we generated artificial neural networks capable of predicting the affinity of TAP for random sequence 9-mer peptides. Using neural network-based predictions of TAP affinity, we found that peptides eluted from three different HLA class I molecules had higher TAP affinities than control peptides with equal binding affinities for the same HLA class I molecules, suggesting that human TAP may contribute to epitope selection. In simulated TAP binding experiments with 408 HLA class I binding peptides, HLA class I molecules differed significantly with respect to TAP affinities of their ligands, As a result, some class I molecules, especially HLA-B27, may be particularly efficient in presentation of cytosolic peptides with low concentrations, while most class I molecules may predominantly present abundant cytosolic peptides.

Inhibition of NK cells by murine CMV-encoded class I MHC homologue m144

Murine cytomegalovirus (CMV)-encoded protein m144 is homologous to class I MHC heavy-chain and is thought to regulate NK-cell-mediated immune responses in vivo. To examine the effects of m144 on Nh cytotoxicity in vitro, various cell lines were transfected with wild-type m144 or a chimeric construct in which the cytoplasmic domain of m144 was replaced with green fluorescence protein. Burkitt lymphoma line Raji expressed a significant level of m144 as determined by anti-m144 mAb binding or the green fluorescence of the fusion protein. The level of m144 expression was relatively low compared with that of transfected murine class I MHC Dd. However, m144 on Raji cells partially inhibited antibody-dependent cell-mediated cytotoxicity of IL-2-activated NK cells. NK cells from the CMV-susceptible BALB/c as well as those from the resistant C57BL/6 mice were inhibited by m144. Antibodies against the known murine NK inhibitory receptors Ly-49A, C, G, and I did not affect the inhibitory effect of m144. These results suggest that the murine CMV class I MHC homologue m144 partially inhibits MZ cells by interacting with a novel inhibitory receptor. (C) 1999 Academic Press.

Cytomegalovirus MHC class I homologues and natural killer

Viruses that establish a persistent infection with their host have evolved numerous strategies to evade the immune system. Consequently, they are useful tools to dissect the complex cellular processes that comprise the immune response. Rapid progress has been made in recent years in defining the role of cellular MHC class I molecules in regulating the response of natural killer (NK) cells. Concomitantly, the roles of the MHC class I homologues encoded by human and mouse cytomegaloviruses in evading or subverting NK cell responses has received considerable interest. This review discusses the results from a number of studies that have pursued the biological function of the viral MHC class I homologues. Based on the evidence from these studies, hypotheses for the possible role of these intriguing molecules are presented. (C) 2000 Editions scientifiques et medicales Elsevier SAS.

MHC class II expression is regulated in dendritic cells independently of invariant chain degradation

We have investigated the mechanisms that control MHC class II (MHC II) expression in immature and activated dendritic cells (DC) grown from spleen and bone marrow precursors. Degradation of the MHC II chaperone invariant chain (li), acquisition of peptide cargo by MHC II, and delivery of MHC II-peptide complexes to the cell surface proceeded similarly in both immature and activated DC. However, immature DC reendocytosed and then degraded the MHC II-peptide complexes much faster than the activated DC. MHC II expression in DC is therefore not controlled by the activity of the protease(s) that degrade Ii, but by the rate of endocytosis of peptide-loaded MHC II. Late after activation, DC downregulated MHC II synthesis both in vitro and in vivo.

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. multipred is 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. multipred replaces 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.

PREDBALB/c: a system for the prediction of peptide binding to H2(d) molecules, a haplotype of the BALB/c mouse

PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I ( H2-K-d, H2-L-d and H2-D-d) and class II (I-E-d and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PREDBALB/c is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.