Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach

Abstract

The overexpression of matrix metalloproteinase-9 (MMP-9) 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 MMP-9 targets, an in silico study was presented to investigate whether our compounds inhibit MMP-9 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 the CHEMBL321 dataset of MMP-9 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 9,102 compounds to seek novel compounds as MMP-9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of the reference molecule. Furthermore, molecules having the highest docking scores were selected, and interaction mechanisms with respect to S1 pocket and catalytic zinc ion of these compounds were also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP-9, were considered preferentially for molecular dynamics studies (100 ns) and an MM-PBSA (last 30 ns) analysis. Based on the results, we proposed several novel compounds as potential candidates for MMP-9 inhibition and investigated their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP-9 inhibitors in the future.