Improved 3D-QSAR Prediction by Multiple Conformational Alignments and Molecular Docking Studies to Design and Discover HIV-I Protease Inhibitors

Background: Inhibition of HIV-I protease enzyme is a strategic step for providing better treatment in retrovirus infections, which avoids resistance and possesses less toxicity. Objectives: In the course of our research to discover new and potent protease inhibitors, 3D-QSAR (CoMFA and CoMSIA) models were generated using 3 different alignment techniques, including multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules were designed from the output of this study. Methods: A total of 3 alignment methods were used to generate CoMFA and CoMSIA models. A Distill based alignment method was considered a better method according to different validation parameters. A 3D-QSAR model was generated and contour maps were discussed. The biological activity of designed molecules was predicted using the generated QSAR model to validate QSAR. The newly designed molecules were docked to predict binding affinity. Results: In CoMFA, leave one out cross-validated coefficient (q(2)), conventional coefficient (r(2)) and predicted correlation coefficient (r(Predicted)(2)) values were found to be 0.721, 0.991 and 0.780, respectively. The best obtained CoMSIA model also showed significant cross-validated coefficient (q(2)), conventional coefficient (r(2)) and predicted correlation coefficient (r(Predicted)(2)) values of 0.714, 0.987 and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models were used to design new and bioactive protease inhibitors by incorporating bioisosterism and knowledge-based structure-activity relationship. Conclusion: The results from both these approaches, ligand-based drug design and structure-based drug design, are adequate and promising to discover protease inhibitors.