Predictive 3D-QSAR and HQSAR model generation of isocitrate lyase (ICL) inhibitors by various alignment methods combined with docking study

Abstract

Isocitrate lyase (ICL) is one of the most important targets in the treatment of Mycobacterium tuberculosis. In this study a diverse set of 2-benzanilide derivatives were aligned by two different methods for CoMFA, CoMSIA, and HQSAR analysis. The best CoMFA model was obtained with the internal validation value (q2) of 0.730 and conventional coefficient (r2) of 0.944. Various CoMSIA models were generated and cross-validated. The best cross-validation coefficient (q2) value was found to be statistically satisfactory (0.688). Both the models were validated by test set of 10 compounds with satisfactory prediction value of (r2pred) 0.725 and 0.631 for CoMFA and CoMSIA, respectively. Cross-validation coefficient value (q2) of 0.694 and r2 of 0.856 were obtained for HQSAR study. The docking study reveals that large hydrophobic pockets occupy R substitutions of these compounds. An electronically negative surface is observed near R1 substitution. The results of the 3D-QSAR analysis corroborate with the molecular docking results, and our findings will serve as a basis for further development of better allosteric inhibitors of ICL inhibitors against M. tuberculosis.