title: SVR-PAIRWISE method to predict MHC-II binding peptides.
abstract: Peptides binding to MHC molecules is of great importance in the process of triggering and initiating immune responses. There are two main kinds of MHC molecules, MHC class I and class II, and the prediction of MHC-II binding peptides is much more difficult due to their variable lengths, which makes it difficult to construct a preferable prediction model by using most of the existing methods. This paper presents a method, called as SVR-PAIRWISE, to combine Support Vector Regression (SVR) and pairwise alignment, to quantitatively predict the MHC-II binding peptides. The comparison results with some popular methods show its satisfying performances.
authors.affiliation: School of Computer, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. wl791014@whu.edu.cn
authors.forename: Juan
authors.initials: J
authors.lastname: Liu
authors.forename: Lian
authors.initials: L
authors.lastname: Wang
authors.forename: Shanfeng
authors.initials: S
authors.lastname: Zhu
mesh_terms: D001665:Binding Sites; D030562:Databases, Protein; D000949:Histocompatibility Antigens Class II / Q000737:chemistry* / Q000378:metabolism*; D009842:Oligopeptides / Q000737:chemistry* / Q000378:metabolism*; D012044:Regression Analysis; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D000949:Histocompatibility Antigens Class II; C408902:MHC binding peptide; D009842:Oligopeptides