Predicting MHC-II binding affinity using multiple instance regression.

ABSTRACT: Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on th

SUBMITTER: EL-Manzalawy Y 

PROVIDER: S-EPMC3400677 | biostudies-literature | 2011 Jul-Aug

REPOSITORIES: biostudies-literature