SVRMHC prediction server for MHC-binding peptides.
BACKGROUND
The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.


RESULTS
Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods.


CONCLUSIONS
SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.
Wan
Ji
J
Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. wanji@biocompute.umn.edu
Liu
Wen
W
Xu
Qiqi
Q
Ren
Yongliang
Y
Flower
Darren R
DR
Li
Tongbin
T
D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D018604:Epitope Mapping / Q000379:methods*; D000939:Epitopes / Q000737:chemistry* / Q000276:immunology*; D020407:Internet; D008285:Major Histocompatibility Complex / Q000276:immunology*; D008956:Models, Chemical; D018448:Models, Immunological; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
D000939:Epitopes