Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches.

Junjie Zhong, Wendi Xuan, Sheng Lu, Shihao Cui, Yuhang Zhou, Mengting Tang, Xiaosheng Qu, Wencong Lu, Haizhong Huo, Chi Zhang, Ning Zhang, Bing Niu

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences 2023 May 01

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Calcium-activated chloride channels (CaCCs) are chloride channels that are regulated according to intracellular calcium ion concentrations. The channel protein ANO1 is widely present in cells and is involved in physiological activities including cellular secretion, signaling, cell proliferation and vasoconstriction and diastole. In this study, the ANO1 inhibitors were investigated with machine learning and molecular simulation. Two-dimensional structure-activity relationship (2D-SAR) and three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for the qualitative and quantitative prediction of ANO1 inhibitors. The results showed that the prediction accuracies of the model were 85.9% and 87.8% for the training and test sets, respectively, and 85.9% and 87.8% for the rotating forest (RF) in the 2D-SAR model. The CoMFA and CoMSIA methods were then used for 3D QSAR modeling of ANO1 inhibitors, respectively. The q2 coefficients for model cross-validation were all greater than 0.5, implying that we were able to obtain a stable model for drug activity prediction. Molecular docking was further used to simulate the interactions between the five most promising compounds predicted by the model and the ANO1 protein. The total score for the docking results between all five compounds and the target protein was greater than 6, indicating that they interacted strongly in the form of hydrogen bonds. Finally, simulations of amino acid mutations around the docking cavity of the target proteins showed that each molecule had two or more sites of reduced affinity following a single mutation, indicating outstanding specificity of the screened drug molecules and their protein ligands. Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Citation

Junjie Zhong, Wendi Xuan, Sheng Lu, Shihao Cui, Yuhang Zhou, Mengting Tang, Xiaosheng Qu, Wencong Lu, Haizhong Huo, Chi Zhang, Ning Zhang, Bing Niu. Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences. 2023 May 01;184:106408