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Nucleic Acids Research Advance Access published online on May 7, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn202
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11

Claus Lundegaard1,*, Kasper Lamberth2, Mikkel Harndahl2, Søren Buus2, Ole Lund1 and Morten Nielsen1

1CBS, Department of Systems Biology, Technical University of Denmark DTU, Kemitorvet Build. 208, 2800 Lyngby and 2Department of International Health, Immunology and Microbiology, University of Copenhagen, Panum Institute 22.3.6, Blegdamsvej 18, 2200 Copenhagen N, Denmark

*To whom correspondence should be addressed. Tel: +45 21900767; Fax: +45 45931585; Email: lunde{at}cbc.dtu.dk

Received January 31, 2008. Revised March 27, 2008. Accepted April 4, 2008.

NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8–11 for all 122 alleles. artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.


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