MetaMHC: a meta approach to predict peptides binding to MHC molecules

As antigenic peptides binding to major histocompatibility complex (MHC) molecules is the prerequisite of cellular immune responses, an accurate computational predictor will be of great benefit to biologists and immunologists for understanding the underlying mechanism of immune recognition as well as facilitating the process of epitope mapping and vaccine design. Although various computational approaches have been developed, recent experimental results on benchmark data sets show that the development of improved predictors is needed, especially for MHC Class II peptide binding. To make the most of current methods and achieve a higher predictive performance, we developed a new web server, MetaMHC, to integrate the outputs of leading predictors by several popular ensemble strategies. MetaMHC consists of two components: MetaMHCI and MetaMHCII for MHC Class I peptide and MHC Class II peptide binding predictions, respectively. Experimental results by both cross-validation and using an independent data set show that the ensemble approaches outperform individual predictors, being statistically significant. MetaMHC is freely available at http://www.biokdd.fudan.edu.cn/Service/MetaMHC.html

Improving peptide-MHC class I binding prediction for unbalanced datasets

<p>Abstract</p> <p>Background</p> <p>Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI) is a crucial step in the development of subunit vaccines and prediction of such binding could greatly reduce costs and accelerate the experimental process of identifying immunogenic peptides. Many methods have been applied to the prediction of peptide-MHCI binding, with some achieving outstanding performance. Because of the experimental methods used to measure binding or affinity between peptides and MHCI molecules, however, available datasets are enriched for nonbinders, and thus highly unbalanced. Although there is no consensus on the ideal class distribution for training sets, extremely unbalanced datasets can be detrimental to the performance of prediction algorithms.</p> <p>Results</p> <p>We have developed a decision-theoretic framework to construct cost-sensitive trees to predict peptide-MHCI binding and have used them to 1) Assess the impact of the training data's class distribution on classifier accuracy, and 2) Compare resampling and cost-sensitive methods as approaches to compensate for training data imbalance. Our results confirm that highly unbalanced training sets can reduce the accuracy of classifier predictions and show that, in the peptide-MHCI binding context, resampling methods do not improve the classifier performance. In contrast, cost-sensitive methods significantly improve accuracy of decision trees. Finally, we propose the use of a training scheme that, when the training set is enriched for nonbinders, consistently improves the overall classifier accuracy compared to cost-insensitive classifiers and, in particular, increases the sensitivity of the classifiers. This method minimizes the expected classification cost for large datasets.</p> <p>Conclusion</p> <p>Our method consistently improves the performance of decision trees in predicting peptide-MHC class I binding by using cost-balancing techniques to compensate for the imbalance in the training dataset.</p

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan

Prediction of MHC-peptide binding: a systematic and comprehensive overview

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks

Stability and expression levels of HLA-C on the cell membrane modulate HIV-1 infectivity

HLA-C expression is associated with a differential ability to control HIV-1 infection. Higher HLA-C levels may lead to a better control of HIV-1 infection through both a higher efficiency of antigen presentation to cytotoxic T lymphocytes (CTLs), as well as the triggering of activating Killer Immunoglobulin like receptors (KIR) on NK-cells, whereas lower levels may provide a poor HIV-1 control and a rapid progression toward AIDS.We characterized the relative amount of HLA-C heterotrimers (heavy chain/\u3b22m/peptide) and HLA-C free heavy chains on PBMC from healthy blood donors harboring both alleles with stable or unstable binding to \u3b22m/peptide. We analyzed the stability of HLA-C heterotrimers of different allotypes and the infectivity of HIV-1 virions produced by PBMC with various allotypes.We observed significant differences in HLA-C heterotrimers stability and in expression levels. We found that R5 HIV-1 virions produced by PBMC harboring unstable HLA-C alleles were more infectious than those produced by PBMC carrying the stable variants.We propose that HIV-1 infectivity might depend both on the amounts of HLA-C molecules and on their stability as trimeric complex. According to this model, individuals with low expressed HLA-C alleles and unstable binding to \u3b22m/peptide might have a worse control of HIV-1 infection and an intrinsically higher capacity to support viral replication.IMPORTANCE Following HIV-1 infection, some people advance rapidly toward AIDS while others have a slow disease progression. HLA-C, a molecule involved in immunity, is a key determinant of HIV-1 control.Here we reveal how HLA-C variants contribute to modulate viral infectivity. HLA-C is present on the cell surface in two different conformations: the immunologically active conformation is part of a complex that includes \u3b22-microglobulin/peptide; the other conformation is not bound to \u3b22-microglobulin/peptide and can associate with HIV-1, increasing its infectivity. Individuals with HLA-C variants with a predominance of immunologically active conformations would display a stronger immunity against HIV-1, a reduced viral infectivity and an effective control of HIV-1 infection, while subjects with HLA-C variants that easily dissociate from \u3b22-microglobulin/peptide would have a reduced immunological response to HIV-1 and produce more infectious virions.This study provides new information that could be useful to design novel vaccine strategies and therapeutic approaches against HIV-1