Predicting AAK1/GAK Dual-Target Inhibitor against SARS-CoV-2 Viral Entry into Host Cells: An in silico Approach

Abstract

Clathrin-mediated endocytosis (CME) is a normal biological process where cellular contents are transported into the cells. However, this process is often hijacked by different viruses to enter host cells and cause infections. Recently, two proteins that regulate CME – AAK1 and GAK – have been proposed as potential therapeutic targets for designing broad-spectrum antiviral drugs. In this work, we curated two compound datasets containing 83 AAK1 inhibitors and 196 GAK inhibitors each. Subsequently, machine learning methods, namely Random Forest, Elastic Net and Sequential Minimal Optimization, were used to construct Quantitative Structure Activity Relationship (QSAR) models to predict small molecule inhibitors of AAK1 and GAK. To ensure predictivity, these models were evaluated by using Leave-One-Out (LOO) cross validation and with an external test set. In all cases, our QSAR models achieved a q²_LOO in range of 0.64 to 0.84 (Root Mean Squared Error; RMSE = 0.41 to 0.52) and a q²_ext in range of 0.57 to 0.92 (RMSE = 0.36 to 0.61). Besides, our QSAR models were evaluated by using additional QSAR performance metrics and y-randomization test. Finally, by using a concensus scoring approach, nine chemical compounds from the Drugbank compound library were predicted as AAK1/GAK dual-target inhibitors. The electrostatic potential maps for the nine compounds were generated and compared against two known dual-target inhibitors, sunitinib and baricitinib. Our work provides the rationale to validate these nine compounds experimentally against the protein targets AAK1 and GAK.