Bioinformatics Advance Access published online on February 12, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth100
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Lyngby, Denmark
* To whom correspondence should be addressed. E-mail: mniel{at}cbs.dtu.dk.
Motivation: Prediction of which peptides that will bind a specific MHC complex constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating binding such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we whish to describe a novel Gibbs motif sampler method ideally suited to recognize such weak sequence motifs. The method is based on the Gibbs sampling method previously described by Lawrence et al. (1993), and it incorporates novel features optimized for the task of recognizing the binding motif of MHC class I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight matrix can be applied to effectively identify potential MHC binding peptides and to guide the process of rational vaccine design. Results: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP, and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation the performance is quantified using ROC curve plots and we make a detailed comparison of the performance to that of both the TEPITOPE method and a weight matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.
Revised November 21, 2003
Accepted December 9, 2003
Article
Improved prediction of MHC class I and II epitopes using a novel Gibbs sampling approach
2 Department of Experimental Immunology, Institute of Medical Microbiology and Immunology, Panum Building 18.3.22, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark
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