POPISK: T-cell reactivity prediction using support vector machines and string kernels

BMC Bioinformatics

Research Article

Andreas Kämper¹  Matthias Ziehm¹  Oliver Kohlbacher¹  Shinn-Ying Ho²  Chun-Wei Tung³ 

[1] Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, 72076, Tübingen, Germany;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, 300, Hsinchu, Taiwan;School of Pharmacy, Kaohsiung Medical University, 807, Kaohsiung, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;

关键词: Major Histocompatibility Complex;    Major Histocompatibility Complex Class;    Transporter Associate With Antigen Processing;    Area Under Receiver Operate Characteristic Curve;    String Kernel;   

DOI  :  10.1186/1471-2105-12-446

 received in 2011-07-26, accepted in 2011-11-15,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundAccurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity.ResultsThis work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction.ConclusionsA computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK.

【 授权许可】

Unknown   
© Tung et al; licensee BioMed Central Ltd. 2011. This article is published under license to 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.