title: In silico study combining docking and QSAR methods on a series of matrix metalloproteinase 13 inhibitors.
abstract: Matrix metalloproteinase 13 (MMP-13) plays an important role in the degradation of articular cartilage and has been considered as an attractive target for the treatment of osteoarthritis; hence, the development of efficient inhibitors of MMP-13 has become a hot study field. Taking a series of carboxylic acid-based MMP-13 inhibitors as research object, this work utilized an extended QSAR method to analyze the structure-activity relationships. We focused on two important topics in QSAR: bioactive conformation and descriptors. Firstly, molecular docking was carried out to dock all molecules into the MMP-13 active site in order to obtain the bioactive conformation. Secondly, based on the docked complex, descriptors characterizing receptor-ligand interactions and the ligand structure were calculated. Thirdly, a genetic algorithm (GA) and multiple linear regression (MLR) were employed to select important descriptors related to inhibitory activities, simultaneously, to build the predictive model. The built model gave satisfactory results with highly accurate fitting and strong external predictive abilities for chemicals not used in model development. Furthermore, the selected descriptors were explored to elucidate important factors influencing the inhibition activities. This study demonstrates that the selection strategy of the docking-guided bioactive conformation is rational and useful in predicting MMP-13 inhibitor activities, and receptor-ligand complex descriptors have an advantage over directly reflecting receptor-ligand interactions.
authors.affiliation: Department of Pharmacy, First Hospital of Lanzhou University, Lanzhou, China.
authors.forename: Lili
authors.initials: L
authors.lastname: Xi
authors.forename: Shuyan
authors.initials: S
authors.lastname: Li
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Yao
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Wei
authors.forename: Jiazhong
authors.initials: J
authors.lastname: Li
authors.forename: Huanxiang
authors.initials: H
authors.lastname: Liu
authors.forename: Xin'an
authors.initials: X
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D002264:Carboxylic Acids / Q000737:chemistry / Q000494:pharmacology*; D017076:Computer-Aided Design; D015195:Drug Design; D008024:Ligands; D016014:Linear Models; D053509:Matrix Metalloproteinase 13 / Q000737:chemistry / Q000187:drug effects*; D061965:Matrix Metalloproteinase Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D062105:Molecular Docking Simulation; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
chemical_list: D002264:Carboxylic Acids; D008024:Ligands; D061965:Matrix Metalloproteinase Inhibitors; D053509:Matrix Metalloproteinase 13
keywords: Genetic algorithm; Matrix metalloproteinase 13 inhibitors; Molecular docking; Multiple linear regression