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Bioinformatics Advance Access published online on April 13, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl141
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 23, 2005
Revised March 8, 2006
Accepted April 8, 2006

Article

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

Shanfeng Zhu 1, Keiko Udaka 2, John Sidney 3, Alessandro Sette 3, Kiyoko F. Aoki-Kinoshita 1, and Hiroshi Mamitsuka 1 *

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan
2 Department of Immunology, Kochi Medical School, Nankoku, Kochi 783-8505, Japan
3 La Jolla Institute for Allergy and Immunology, 10335 Science Center Drive, La Jolla, CA 92121, USA

* To whom correspondence should be addressed.
Hiroshi Mamitsuka, E-mail: mami{at}kuicr.kyoto-u.ac.jp


  Abstract

Motivation: Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules.

Results: We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801), and A24 supertype (A*2301 and A*2402), whose data was collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.

Associate Editor: Satoru Miyano
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