title: A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information.
abstract: MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.
authors.affiliation: School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China.
authors.forename: Yilei
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China.
authors.forename: Gaofeng
authors.initials: G
authors.lastname: Pan
authors.affiliation: School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China; School of Computational Science and Engineering, University of South Carolina, Columbia, SC, USA.
authors.forename: Jijun
authors.initials: J
authors.lastname: Tang
authors.affiliation: School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Guo
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D015195:Drug Design; D018604:Epitope Mapping / Q000379:methods*; D000939:Epitopes / Q000737:chemistry* / Q000276:immunology*; D006684:HLA-DR Antigens / Q000737:chemistry* / Q000276:immunology* / Q000648:ultrastructure; D062105:Molecular Docking Simulation / Q000379:methods*; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry / Q000276:immunology; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods
chemical_list: D000939:Epitopes; D006684:HLA-DR Antigens; D010455:Peptides