Discovery of new β-D-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening.
Glycosidases, including β-D-glucosidase, are involved in a variety of metabolic disorders such as diabetes, viral or bacterial infections and cancer. Accordingly, we were prompted to find new β-D-glucosidase inhibitors. Towards this end we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Three pharmacophores emerged in the QSAR equations, suggesting the existence of more than one binding mode accessible to ligands within the β-D-glucosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic (ROC) curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were established experimentally by the identification of several β-D-glucosidase inhibitors retrieved via in silico search of two structural databases, namely the National Cancer Institute (NCI) list of compounds, and our in-house structural database of established drugs and agrochemicals (DAC).
Abu Khalaf
Reema
R
Department of Pharmaceutical Sciences, Faculty of Pharmacy, Al Zaytoonah Private University of Jordan, Amman, Jordan.
Abdula
Ahmed Mutanabbi
AM
Mubarak
Mohammad S
MS
Taha
Mutasem O
MO
D001665:Binding Sites; D019295:Computational Biology; D004791:Enzyme Inhibitors / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D008968:Molecular Conformation; D017434:Protein Structure, Tertiary; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D001617:beta-Glucosidase / Q000037:antagonists & inhibitors* / Q000737:chemistry
D004791:Enzyme Inhibitors; D001617:beta-Glucosidase