MHCPred : a server for quantitative prediction of peptide-MHC binding

Pingping Guan, Irini A. Doytchinova, Christianna Zygouri, Darren R. Flower

Research output: Contribution to journalArticlepeer-review


Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at
large, we have implemented, as a server on the World Wide Web, a partial least squares-base multivariate statistical approach to the quantitative prediction of peptide binding to major histocom-patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive,cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203,HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401and HLA-DRB* 0701).
Original languageEnglish
Pages (from-to)3621-3624
Number of pages4
JournalNucleic Acids Research
Issue number13
Publication statusPublished - Jul 2003


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