title: 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 this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark data sets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir.
authors.affiliation: Department of Systems and Computers Engineering, Al-Azhar University, Cairo, Egypt. yasser@azhar.edu.eg
authors.forename: Yasser
authors.initials: Y
authors.lastname: EL-Manzalawy
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000737:chemistry / Q000378:metabolism*; D005802:Genes, MHC Class II; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D012044:Regression Analysis; D015203:Reproducibility of Results; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D004268:DNA-Binding Proteins; D010455:Peptides; D014157:Transcription Factors