Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach - Mathpal, Shalini; Sharma, Priyanka; Joshi, Tushar; Pande, Veena; Mahmud, Shafi; Jeong, Mi-Kyung; Obaidullah, Ahmad J.; Chandra, Subhash; Kim, Bonglee (Frontiers in Molecular Biosciences, 2022)
The overexpression of Matrix metalloproteinases 9 (MMP9) is associated with tumor development and angiogenesis and hence it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP9 targets, an in silico study is presented to investigate whether our compounds inhibit the MMP9 by binding to the catalytic domain, similar to their inhibitor or not. For that, in the initial stage, a deep-learning algorithm was used for the predictive modeling of CHEMBL321 dataset of MMP9 inhibitors. Several regression models were built and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model was utilized to screen the drug bank database containing 9102 compounds to seek novel compounds as MMP9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of reference molecule. Further, molecules having the highest docking scores were selected and interaction mechanisms with respect to S1' pocket and catalytic zinc ion of these compounds are also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP9, were considered preferentially for Molecular dynamics studies (100 ns) and MM-PBSA (last 30 ns) analysis. Based on the results, we propose several novel compounds as potent candidates for MMP9 inhibition and investigate their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP9 inhibitors in the future.