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Bioinformatics Advance Access published online on November 7, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn579
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Pan-specific MHC class I predictors: A benchmark of HLA class I pan-specific prediction methods

Hao Zhang , Claus Lundegaard and Morten Nielsen *

Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby 2800, Denmark

*To whom correspondence should be addressed. Dr. Morten Nielsen, E-mail: mniel{at}cbs.dtu.dk


  Abstract

Motivation: MHC:peptide binding plays a central role in activating the immune surveillance. Computational approaches to determine T-cell epitopes restricted to any given MHC molecule are of special practical value in the development of for instance vaccines with broad population coverage against emerging pathogens. Methods have recently been published that are able to predict peptide binding to any human MHC class I molecule. In contrast to conventional allele-specific methods, these methods do allow for extrapolation to un-characterized MHC molecules. These pan-specific HLA predictors have not previously been compared using independent evaluation sets.

Results: A diverse set of quantitative peptide binding affinity measurements was collected from IEDB, together with a large set of HLA class I ligands from the SYFPEITHI database. Based on these data sets, three different pan-specific HLA web-accessible predictors NetMHCpan, Adaptive-Double-Threading (ADT), and KISS were evaluated. The performance of the pan-specific predictors was also compared to a well performing allele-specific MHC class I predictor, NetMHC, as well as a consensus approach integrating the predictions from the NetMHC and NetMHCpan methods.

Conclusions: The benchmark demonstrated that pan-specific methods do provide accurate predictions also for previously uncharacterized MHC molecules. The NetMHCpan method trained to predict actual binding affinities was consistently top ranking both on quantitative (affinity) and binary (ligand) data. However, the KISS method trained to predict binary data was one of the best performing when benchmarked on binary data. Finally, a consensus method integrating predictions from the two best-performing methods was shown to improve the prediction accuracy.

Associate Editor: Prof. Thomas Lengauer

Received on May 1, 2008; revised on September 23, 2008; accepted on November 6, 2008

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