SVRMHC prediction server for MHC-binding peptides

Wan, Ji, Liu, Wen, Xu, Qiqi, Ren, Yongliang, Flower, Darren R. and Li, Tongbin (2006). SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics, 7 , p. 463.

Abstract

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Publication DOI: https://doi.org/10.1186/1471-2105-7-463
Divisions: Life & Health Sciences > Pharmacy
Life & Health Sciences
Additional Information: © 2006 Wan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Full Text Link: http://www.biom ... 1471-2105/7/463
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Published Date: 2006-10-23
Authors: Wan, Ji
Liu, Wen
Xu, Qiqi
Ren, Yongliang
Flower, Darren R. ( 0000-0002-8542-7067)
Li, Tongbin

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License: Creative Commons Attribution


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