Computer-aided drug design to discover DNMT inhibitors from phytochemicals

Abstract

Inhibitors of DNA methyltransferase (DNMTs) are now a major family of epigenetic targets with therapeutic interest. However, only two cytosine analogues 5-azacytosine (azacytidine) and 20-deoxy-5-azacytidine (decitabine), have been approved as the most cutting-edge medications for treating epigenetic cancer with some restrictions. In this context, computational methods that rely on quantitative structure-activity relationship (QSAR) play a crucial role allowing us to predict the biological activity of potential molecules based on the theoretically calculated physicochemical properties of these compounds. When coupled with machine learning (ML), QSAR approaches create an ideal platform for discovering potential drug candidates. In this study, three Machine Learning (ML) models; Random Forest, Support Vector Machine, and Artificial Neural Network, were trained using modified TeachOpenCADD KNIME workflows and applied it to the identification of plant molecules that are structurally similar to the active pharmaceuticals of current DNMT inhibitors. Then molecular docking simulations were performed using AutoDock Vina, employing two human DNMT structures (PDB codes: 4WXX and 2QRV) as target proteins and the predicted phytochemicals as ligands. Additionally, we focused on the R882H mutation hotspot in the catalytic domain of DNMT3A, which is associated with aberrant DNA methylation in acute myeloid leukemia (AML). Consequently, the structure of R882H DNMT3A (PDB code: 6W8J) was docked with the identified novel ligands. As a result of our computational analysis, eight phytochemicals were predicted as potential DNMT inhibitors through the ML approaches from KNIME. Subsequently, three of these phytochemicals, namely Herbacetin, Kaempferide, and Morin were identified as virtual hits against DNMTs following the molecular docking simulations. Overall, our study demonstrates the effectiveness of this computational strategy in identifying DNMT inhibitors. These findings hold promise for the discovery of potent and selective anticancer drugs targeting DNMTs.