Cited By
View all- Soria-Guerra RNieto-Gomez RGovea-Alonso DRosales-Mendoza S(2015)An overview of bioinformatics tools for epitope predictionJournal of Biomedical Informatics10.1016/j.jbi.2014.11.00353:C(405-414)Online publication date: 1-Feb-2015
Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers
Abstract
Summary: Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (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.
Availablility: 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
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online
Index Terms
- Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers
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Oxford University Press, Inc.
United States
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Published: 01 June 2008
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View all- Soria-Guerra RNieto-Gomez RGovea-Alonso DRosales-Mendoza S(2015)An overview of bioinformatics tools for epitope predictionJournal of Biomedical Informatics10.1016/j.jbi.2014.11.00353:C(405-414)Online publication date: 1-Feb-2015