Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

Journal: Molecular diversity
Published Date:

Abstract

Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certain cancers, while clinically approved FXR antagonists remain unavailable and underrepresented in current treatment strategies. To address this, we developed deep learning models for predicting FXR antagonistic activity (ANTCL) and toxicity (TOXCL). Screening 217,345 compounds from the HMDB database identified eleven human metabolite candidates with significant FXR binding potential. Molecular dynamics simulations and binding free energy calculations revealed five more stable complexes compared to the reference compound Gly-MCA, with HMDB0253354 (Fulvestrant) and HMDB0242367 (ZM 189154) standing out for their binding free energies. Hydrophobic interactions, particularly involving residues MET328, PHE329, and ALA291, contributed to their stability. These results demonstrate the effectiveness of deep learning in FXR antagonist discovery and highlight the potential of HMDB0253354 and HMDB0242367 as promising candidates for metabolic disease treatment.

Authors

  • Yueying Yang
    School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
  • Yuxin Huang
  • Hanxiao Shen
    Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China.
  • Ding Wang
  • Zhen Liu
    School of Pharmacy, Fudan University, PR China; Analytical Service Unit, WuXi AppTec (Shanghai) Co., Ltd, Shanghai, 200131, PR China.
  • Wei Zhu
    The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine Guangzhou 510120 China zhuwei9201@163.com.
  • Qing Liu
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China.