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Bioinformatics Advance Access published online on April 14, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn128
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.

Claus Lundegaard 1,*, Ole Lund 1 and Morten Nielsen 1

1Center for Biological Sequence Analysis – CBS, Department of Systems Biology, The Technical University of Denmark – DTU, Kemitorvet Build. 208, 2800 Lyngby, DENMARK

*To whom correspondence should be addressed. Dr. Claus Lundegaard, E-mail: lunde{at}cbs.dtu.dk


  Abstract

Summary: Several accurate prediction systems have been developed for prediction of class I MHC:peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides.

Availability: The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

Associate Editor: Prof. Burkhard Rost

Received on February 8, 2008; revised on April 4, 2008; accepted on April 4, 2008

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