Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Hu X

PROVIDER: S-EPMC2896142 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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

Hu Xihao X Zhou Wenjian W Udaka Keiko K Mamitsuka Hiroshi H Zhu Shanfeng S

Nucleic acids research 20100518 Web Server issue

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 develop ...[more]

PMID: 20483919

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Project description:BackgroundPeptides 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. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA) for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs.ResultsThe proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule - a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1) an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2) quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules.ConclusionWe present two MOEA-based algorithms for finding motifs, one for self-discovery and the other for guided-discovery by experimentally determined motifs, and thereby predicting binding peptides to I-Ag7 molecule. Our experiments show that the proposed MOEA-based algorithms are better than earlier methods in predicting binding sites not only on I-Ag7 but also on most alleles of class II MHC benchmark datasets. This shows that our methods could be applicable to find binding motifs in a wide range of alleles.

Dataset Information

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

Publications

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

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