Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors. (October 2018)

• Record Type: Journal Article
• Authors: Asadollahi-Baboli, M.; Dehnavi, S.
• Is Part Of: Computational biology and chemistry. Volume 76(2018)
• Page Start: 283
• Page End: 292
• Publication Date: 2018-10
• Subjects: α-Glucosidase inhibitors -- Classification -- Molecular docking -- GA-PLS -- SVM -- Applicability domain; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.07.019
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Different classes of 0-3D descriptors were computed using the best conformations achieved by molecular docking. Hydrogen bonds and polar contacts have important roles in α-glucosidase inhibition mechanism. Graphical classification of active/inactive α-glucosidase inhibitors were successfully achieved using PLS-DA. p IC50 of tetracyclic oxindoles can be estimated using validated GA-PLS/SVM strategy. Improved new potent inhibitors were designed using obtained QSAR and molecular docking results. Abstract: The α-glucosidase inhibitors are considered as important agents in drug discovery against diabetes mellitus. Molecular docking and quantitative structure-activity relationship (QSAR) were performed based on a series of tetracyclic oxindole derivatives to elucidate key structural properties affecting inhibitory activity and support the design of new α-glucosidase inhibitors. The molecular docking results demonstrate that at least two hydrogen bonds between Thr681 and Arg676 residues and the oxygen atoms in amid groups have an important role in the optimum binding of inhibitors. In addition, the sum of polar contacts of Arg699, Arg670, Glu792 and Glu301 residues with the α-glucosidase inhibitors have more than one third of total binding free energy. The docked conformations of the inhibitors with the best binding free energy were used to construct QSAR models. As a primary survey and a graphical comparing tool, the partial least squares-discriminant analysis (PLS-DA) technique was successfully employed to classify active and inactive inhibitors. The validated QSAR analysis were performed through genetic algorithm-partial least squares (GA-PLS) and support vector machine (SVM) techniques. The QSAR model reveals that important features of J3D, Mor26 u and HOMA have a high predictive capability (R 2 p = 0.837, Q 2 LOO = 0.871, R 2 LSO = 0.790 and r 2 m = 0.758) using GA-PLS/SVM strategy. Generally, the suggested QSAR analysis based on classification, docking and GA-PLS/SVM strategy may help suggest chemical scaffold to design novel oxindole derivatives as α-glucosidase inhibitors.