Theoretical modeling and molecular docking simulation for investigating and evaluating some active compounds as potent anti-tubercular agents against MTB CYP121 receptor

Abstract

Theoretical modeling and molecular docking studies were carried out on cinnamic acid analogues as potent anti-tubercular agents. Theoretical models were developed to investigate the reported observed activities and modify the leading compound with better activity. Five predictive models were generated by employing Genetic Function Approximation (GFA) and Multi-linear Regression (MLR). Based on statistically significant, the optimum model with prominent validation parameters was selected. The selected model was found to have squared correlation coefficient (R2) of 0.942939, adjusted squared correlation coefficient (R2 adj) value of 0.925382 and Leave one out (LOO) cross validation coefficient (Qcv2) value of 0.893486. External validations test were carried out in order to validate the selected model and the model was found to have (R2test) of 0.8612 and Coefficient of determination for Y-randomization (cRp2) value of 0.78571. The docking studies revealed the best molecule with docking scores of −13.7 kcal/mol which formed H-bond and hydrophobic interaction and with amino acid residues M. tuberculosis cytochromes (MTB CYP121). QSAR model generated propose the direction for the design of new anti-tubercular agents via ligand based design while molecular docking results against MTB CYP121 receptor provides a valuable approach for structure based design. Keywords: Anti-tuberculosis, Docking, QSAR, Y-randomization