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

摘要

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-based 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~*0401 and HLA-DRB~*0701). MHCPred is available from the URL: http://www.jenner.ac.uk/MHCPred.