Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: The identification of T-cell epitopes can be crucial for vaccine development. An epitope is a peptide segment that binds to both a T-cell receptor and a major histocompatibility complex (MHC) molecule. Predicting which peptide segments bind MHC molecules is the first step in epitope prediction. RESULTS: An iterative stepwise discriminant analysis meta-algorithm explores a large molecular database to derive quantitative motifs for peptide binding. The applications presented here demonstrate the algorithm's versatility by producing four closely related models for HLA-DR1. Two models use an expert initial estimate and two do not; two models use amino acid residues as the only predictors and two use amino acid groupings as additional predictors. Each model correctly classifies >90% of the peptides in the database. AVAILABILITY: Software is available commercially; data are free over the Internet.     

Iterative stepwise discriminant analysis: a meta-algorithm for detecting quantitative sequence motifs.

An algorithm is presented for detecting a quantitative pattern in peptide fragments that bind class II major histocompatibility complex (MHC) molecules. It is referred to as a meta-algorithm because it requires successive applications of Stepwise Discriminate Analysis (SDA). On every iteration the best subsequence candidates are selected from sequences known to bind class II MHC molecules. When SDA compares probable binding subsequences with subsequences known not to bind class II MHC molecules, a quantitative model emerges that is capable of classifying subsequences as binding or non-binding. In an iterative manner, the resultant model is utilized as a criterion for selecting probable binding subsequence candidates. The procedure is repeated until models converge. In the illustrated examples, the final models correctly classify over 95% of the peptides in a database of peptides whose binding affinity for HLA-DR1 is known. The final model can then be used to predict the binding affinity of peptides that have not yet been laboratory tested.     

Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction

MOTIVATION: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. RESULTS: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 peptides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r(pred) = 0.593 and r(pred) = 0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. AVAILABILITY: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHCPred.     

Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a highly efficient iterative process for improving MHC class II binding predictions.     

Analysis of MHC class II antigen processing by quantitation of peptides that constitute nested sets

Peptides associated with class II MHC molecules are of variable length because in contrast to peptides associated with class I MHC molecules, their amino and C termini are not constrained by the structure of the peptide interaction with the binding site. The proteolytic processing events that generate these peptides are still not well understood. To address this question, peptides extracted from HLA-DR*0401 were analyzed using two types of mass spectrometry instrumentation. This enabled identification of >700 candidate peptides in a single analysis and provided relative abundance information on 142 peptides contained in 11 nested sets of 3-36 members each. Peptides of 12 residues or less occurred only at low abundance, despite the fact that they were predicted to fully occupy the HLA-DR*0401 molecule in a single register. Conversely, the relative abundance of longer species suggested that proteolytic events occurring after MHC binding determine the final structure of most class II-associated peptides. Our data suggest that C-terminal residues of these peptides reflect the action of peptidases that cleave at preferred amino acids, while amino termini appear to be determined more by proximity to the class II MHC binding site. Thus, the analysis of abundance information for class II-associated peptides comprising nested sets has offered new insights into proteolytic processing of MHC class II-associated peptides.     

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Background  

Peptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides.     

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0.     

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.     

Comparative analysis of epitope predictions: proposed library of putative vaccine candidates for HIV

Designing a vaccine for a disease is one of the crucial tasks that involve millions and billions of dollars, several decades and yet there is no guarantee of successful results. Several pharmaceutical companies are investing their money and time in such activities. Computational biology could be of great help in these activities by proving a library of plausible candidates that might actually show some positive responses. MHC binding peptide prediction is one such area where the immense power of computers could be used to get a breakthrough. In this direction several databases and servers have been developed by many labs to predict the MHC binding peptides. These short peptides on the antigen surface are recognized by the MHC molecule and are presented to the receptors of T-cells for further immune response. Peptides that bind to a given MHC molecule share sequence similarity. Here we present a comparative study of servers that can predict the MHC binding peptides in a given protein sequence of the antigen. Based on this comparative analysis on HIV data, we are able to propose a library of putative vaccine candidates for the env GP-160 protein of HIV-1.     

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.     

Use of selected reaction monitoring mass spectrometry for the detection of specific MHC class I peptide antigens on A3 supertype family members

The development of peptide-based vaccines that are useful in the therapeutic treatment of melanoma and other cancers ultimately requires the identification of a sufficient number of antigenic peptides so that most individuals, regardless of their major histocompatibility complex (MHC)–encoded class I molecule phenotype, can develop a cytotoxic T lymphocyte (CTL) response against one or more peptide components of the vaccine. While it is relatively easy to identify antigenic peptides that are presented by the most prevalent MHC class I molecules in the population, it is problematic to identify antigenic peptides that are presented by MHC class I molecules that have less frequent expression in the population. One manner in which this problem can be overcome is by taking advantage of known MHC class I supertypes, which are groupings of MHC class I molecules that bind peptides sharing a common motif. We have developed a mass spectrometric approach which can be used to determine if an antigenic peptide is naturally processed and presented by any given MHC class I molecule. This approach has been applied to the A3 supertype, and the results demonstrate that some, but not all, A3 supertype family–associated peptides can associate with all A3 supertype family members. The approach also demonstrates the shared nature of several newly identified peptide antigens. The use of this technology negates the need to test peptides for their ability to stimulate CTL responses in those cases where the peptide is not naturally processed and bound to the target MHC class I molecule of interest, thus allowing resources to be focused on the most promising vaccine candidates.     

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.     

Peptide binding to HLA-DR1: a peptide with most residues substituted to alanine retains MHC binding

Major histocompatibility complex (MHC) glycoproteins play an important role in the development of an effective immune response. An important MHC function is the ability to bind and present 'processed antigens' (peptides) to T cells. We show here that the purified human class II MHC molecule, HLA-DR1, binds peptides that have been shown to be immunogenic in vivo. Detergent-solubilized HLA-DR1 and a papain-cleaved form of the protein lacking the transmembrane and intracellular regions have similar peptide binding properties. A total of 39 single substitutions were made throughout an HLA-DR1 restricted hemagglutinin epitope and the results determine one amino acid in this peptide which is crucial to binding. Based on this analysis, a synthetic peptide was designed containing two residues from the original hemagglutinin epitope embedded in a chain of polyalanine. This peptide binds to HLA-DR1, indicating that the majority of peptide side chains are not required for high affinity peptide binding.     

Identification and evaluation of universal epitopes in Plasmodium vivax Duffy binding protein

Selected PvDBP-derived synthetic peptides were tested in competition assays with HLA molecules in order to identify and evaluate their binding to a wide range of MHC class II molecules. Binding was evaluated as the peptide’s ability to displace the biotinylated control peptide (HA_306-318) and was detected by a conventional ELISA. Thus, one epitope for the HLA-DR1 molecule, two epitopes for the HLA-DR4 molecule, six epitopes for the HLA-DR7 molecule and three epitopes for the HLA-DR11 molecule displaying a high binding percentage (above 50%) were experimentally obtained. The in vitro results were compared with the epitope prediction results. Two peptides behaved as universal epitopes since they bound to a larger number of HLA-DR molecules. Given that these peptides are located in the conserved PvDBP region II, they could be considered good candidates to be included in the design of a synthetic vaccine against Plasmodium vivax malaria.     

Disruption of Hydrogen Bonds between Major Histocompatibility Complex Class II and the Peptide N-Terminus Is Not Sufficient to Form a Human Leukocyte Antigen-DM Receptive State of Major Histocompatibility Complex Class II

Peptide presentation by MHC class II is of critical importance to the function of CD4+ T cells. HLA-DM resides in the endosomal pathway and edits the peptide repertoire of newly synthesized MHC class II molecules before they are exported to the cell surface. HLA-DM ensures MHC class II molecules bind high affinity peptides by targeting unstable MHC class II:peptide complexes for peptide exchange. Research over the past decade has implicated the peptide N-terminus in modulating the ability of HLA-DM to target a given MHC class II:peptide combination. In particular, attention has been focused on both the hydrogen bonds between MHC class II and peptide, and the occupancy of the P1 anchor pocket. We sought to solve the crystal structure of a HLA-DR1 molecule containing a truncated hemagglutinin peptide missing three N-terminal residues compared to the full-length sequence (residues 306–318) to determine the nature of the MHC class II:peptide species that binds HLA-DM. Here we present structural evidence that HLA-DR1 that is loaded with a peptide truncated to the P1 anchor residue such that it cannot make select hydrogen bonds with the peptide N-terminus, adopts the same conformation as molecules loaded with full-length peptide. HLA-DR1:peptide combinations that were unable to engage up to four key hydrogen bonds were also unable to bind HLA-DM, while those truncated to the P2 residue bound well. These results indicate that the conformational changes in MHC class II molecules that are recognized by HLA-DM occur after disengagement of the P1 anchor residue.     

High non-protective, long-lasting antibody levels in malaria are associated with haplotype shifting in MHC-peptide-TCR complex formation: a new mechanism for immune evasion

An effective malarial vaccine must contain multiple immunogenic, protection-inducing epitopes able to block and destroy the P. falciparum malaria parasite, the most lethal form of this disease in the world. Our strategy has consisted in using conserved peptides blocking parasite binding to red blood cells; however, these peptides are non-immunogenic and non-protection-inducing. Modifying their critical residues can make them immunogenic. Such peptides induced antibody titers (determined by immunofluorescence antibody test, IFA) and made the latter reactive (determined by Western blot) and protection inducing against experimental challenge with a highly infective Aotus monkey adapted P. falciparum strain. Modified peptides also induce highly non-protective long-lasting antibody levels. Modifications performed might allow them to bind specifically to different HLA-DRbeta purified molecules. These immunological and biological activities are associated with modifications in their three-dimensional structure as determined by (1)H-NMR. It was found that modified, high non-protective long-lasting antibody level peptides bound to HLA-DR molecules from a different haplotype (to which immunogenic, protection-inducers bind) and had 4.6 +/- 1.4 A shorter distances between residues fitting into these molecules' Pocket 1 to Pocket 9, suggesting fitting into an inappropriate HLA-DR molecule. A multi-component, subunit-based, malarial vaccine is therefore feasible if modified peptides are suitably modified for an appropriate fit into the correct HLA-DRbeta1* molecule in order to form a proper MHC-II-peptide-TCR complex.     

Binding of longer peptides to the H-2Kb heterodimer is restricted to peptides extended at their C terminus: refinement of the inherent MHC class I peptide binding criteria.

MHC class I molecules usually bind short peptides of 8-10 amino acids, and binding is dependent on allele-specific anchor residues. However, in a number of cellular systems, class I molecules have been found containing peptides longer than the canonical size. To understand the structural requirements for MHC binding of longer peptides, we used an in vitro class I MHC folding assay to examine peptide variants of the antigenic VSV 8 mer core peptide containing length extensions at either their N or C terminus. This approach allowed us to determine the ability of each peptide to productively form Kb/beta2-microglobulin/peptide complexes. We found that H-2Kb molecules can accommodate extended peptides, but only if the extension occurs at the C-terminal peptide end, and that hydrophobic flanking regions are preferred. Peptides extended at their N terminus did not promote productive formation of the trimolecular complex. A structural basis for such findings comes from molecular modeling of a H-2Kb/12 mer complex and comparative analysis of MHC class I structures. These analyses revealed that structural constraints in the A pocket of the class I peptide binding groove hinder the binding of N-terminal-extended peptides, whereas structural features at the C-terminal peptide residue pocket allow C-terminal peptide extensions to reach out of the cleft. These findings broaden our understanding of the inherent peptide binding and epitope selection criteria of the MHC class I molecule. Core peptides extended at their N terminus cannot bind, but peptide extensions at the C terminus are tolerated.     

Functional HLA-DR T cell epitopes of CEA identified in patients with colorectal carcinoma immunized with the recombinant protein CEA

A baculovirus-produced recombinant CEA (rCEA) protein comprising the extracellular region was used for vaccination of CRC patients with or without GM-CSF as an adjuvant cytokine. Ten patients with a significant proliferative T cell response against rCEA were selected for T cell epitope mapping. Fifteen-aa-long overlapping peptides covering the entire aa sequence of the external domain of CEA were used in a proliferation assay. In six of the patients a repeatable T cell response against at least one peptide was demonstrated. For the first time, nine functional HLA-DR epitopes of CEA were defined. Two of the peptides were recognized by more than one patient, i.e., two and three patients, respectively. Those 15-mer peptides that induced a proliferative T cell response fitted to the actual HLA-DR type (SYFPEITHI). The affinity of the native peptides for the T cell receptor was in the low to intermediate range (scores 6–19). The 15-mer peptides also contained 9-mer peptide sequences that could be predicted to bind to the actual HLA-ABC genotypes (SYFPEITHI/BIMAS). Blocking experiments using monoclonal antibodies indicated that the proliferative T cell response was both MHC class I and II restricted. The defined HLA-DR T cell epitopes were spread over the entire CEA molecule, but a higher frequency was noted towards the C-terminal. Peptides with a dual specificity may form a basis for production of subunit cancer vaccines, but modifications should be done to increase the T cell affinity, thereby optimizing the antitumoral effects of the vaccine.Abbreviations aa amino acid - CRC colorectal carcinoma - GM-CSF granulocyte/monocyte colony stimulating factor - CEA carcino-embryonic antigen - BCP baculovirus control protein - MHC major histocompatibility complex - pp peptide - TAA tumor associated antigen     

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.     

TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules

Motivation

Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.

Method

We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.

Result

The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.