Dataset Information

Promiscuous binding of extracellular peptides to cell surface class I MHC protein.

ABSTRACT: Algorithms derived from measurements of short-peptide (8-10 mers) binding to class I MHC proteins suggest that the binding groove of a class I MHC protein, such as K(b), can bind well over 1 million different peptides with significant affinity (<500 nM), a level of ligand-binding promiscuity approaching the level of heat shock protein binding of unfolded proteins. MHC proteins can, nevertheless, discriminate between similar peptides and bind many of them with high (nanomolar) affinity. Some insights into this high-promiscuity/high-affinity behavior and its impact on immunodominant peptides in T-cell responses to some infections and vaccination are suggested by results obtained here from testing a model developed to predict the number of cell surface peptide-MHC complexes that form on cells exposed to extracellular (exogenous) peptides.

SUBMITTER: Eisen HN

PROVIDER: S-EPMC3311345 | biostudies-other | 2012 Mar

REPOSITORIES: biostudies-other

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Promiscuous binding of extracellular peptides to cell surface class I MHC protein.

Eisen Herman N HN Hou Xun Helen XH Shen Chase C Wang Kaidi K Tanguturi Varsha Keelara VK Smith Crysela C Kozyrytska Katerina K Nambiar Lakshmi L McKinley Carol A CA Chen Jianzhu J Cohen Richard J RJ

Proceedings of the National Academy of Sciences of the United States of America 20120307 12

Algorithms derived from measurements of short-peptide (8-10 mers) binding to class I MHC proteins suggest that the binding groove of a class I MHC protein, such as K(b), can bind well over 1 million different peptides with significant affinity (<500 nM), a level of ligand-binding promiscuity approaching the level of heat shock protein binding of unfolded proteins. MHC proteins can, nevertheless, discriminate between similar peptides and bind many of them with high (nanomolar) affinity. Some insi ...[more]

PMID: 22403068

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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

Promiscuous binding of extracellular peptides to cell surface class I MHC protein.

Publications

Promiscuous binding of extracellular peptides to cell surface class I MHC protein.

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