title: QSAR study on MHC class I a alleles based on the novel parameters of amino acids.
abstract: MHC-epitope binding plays a key role in the cellular immune response. Accurate prediction of MHC-epitope binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. In this paper, 13 T descriptors， which derived from 544 physicochemical properties of the natural amino acids, were used to characterize 4 MHC class I alleles epitope peptide sequences, the optimal QSAR models were constructed by using stepwise regression combines with multiple linear regression (STR-MLR). For HLA-A*0201, HLA-A*0203, HLA-A*0206 and HLA-A*1101 alleles, the leave one out cross validation values (Q(2)(train)) were 0.581, 0.553, 0.525 and 0.588, the correlation coefficients (R(2)(train)) of training datasets were 0.607, 0.582, 0.556 and 0.606, the correlation coefficients (R(2)(test)) of test datasets were 0.533, 0.506, 0.501 and 0.502, respectively. The results showed that all models can obtain good performance for prediction and explain the mechanism of interaction between MHC and epitope. The descriptors will be useful in structure characterization and activity prediction of peptide sequences.
authors.affiliation: College of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400050, PR China.
authors.forename: Juan
authors.initials: J
authors.lastname: Wang
authors.forename: Xiao-Yu
authors.initials: XY
authors.lastname: Wang
authors.forename: Mao
authors.initials: M
authors.lastname: Shu
authors.forename: Yuan-Qiang
authors.initials: YQ
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: Lin
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.forename: Xiao-Ming
authors.initials: XM
authors.lastname: Cheng
authors.forename: Zhi-Hua
authors.initials: ZH
authors.lastname: Lin
mesh_terms: D000483:Alleles; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D000939:Epitopes / Q000737:chemistry / Q000276:immunology*; D015234:HLA-A Antigens / Q000737:chemistry / Q000276:immunology*; D015789:HLA-A2 Antigen / Q000737:chemistry / Q000276:immunology; D016014:Linear Models; D008954:Models, Biological; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000596:Amino Acids; D000939:Epitopes; D015234:HLA-A Antigens; C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen