Molecular docking and QSAR analysis of a few Gama amino butyric acid aminotransferase inhibitors

Abstract

Molecular docking and quantitative structure–activity relationship (QSAR) studies were carried out on 37 anticonvulsant compounds to develop a robust model for the prediction of anticonvulsant activities against Gama amino butyric acid aminotransferase (GABA_AT) and to determine the dominant structural amino acid residues responsible for the binding affinity of the ligand-GABA_AT complex. AutoDock Vina of PyRx virtual screening software was used to perform the molecular docking while Genetic function algorithm (GFA) was used to select the descriptors and to generate the correlation models that relate the structural features to the biological activities. The best binding affinity was found to be −11.9 Kcal/mol (compound 5a) while best QSAR model (model 1) was obtained with R² of 0.970192, an R²_adj value of 0.963095, Q²LOO value of 0.947995 and R²pred of 0.813. These confirms the stability, reliability, robustness and predictability of the model. Our research has shown that the binding affinity generated was found to be better than the one reported by another researcher. And the high correlation coefficient, (R²) shows that the model was reliable, robust and predictable. Our QSAR model and molecular docking results corroborate with each other (most especially in the area of binding affinity and atomic electronegativity of the inhibitors) and propose the directions for the design of new inhibitors with better activity against an enzyme that is responsible for epilepsy (GABAAT).

Keywords:

• QSAR
• Gamma-aminobutyric acid aminotransferase
• Molecular docking
• Density Functional Theory
• Anticonvulsants
• Genetic Function Algorithm