FXR拮抗剂发现整合深度学习和分子动力学模拟。

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Yueying Yang, Yuxin Huang, Hanxiao Shen, Ding Wang, Zhen Liu, Wei Zhu, Qing Liu
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引用次数: 0

摘要

法尼类固醇 X 受体(FXR)是胆汁酸、脂质和葡萄糖稳态的关键调节因子,使其成为治疗代谢性疾病的有望靶点。FXR拮抗剂在胆汁淤积症、代谢紊乱和某些癌症中显示出治疗潜力,而临床批准的FXR拮抗剂在目前的治疗策略中仍然不可用且代表性不足。为了解决这个问题,我们开发了用于预测 FXR 拮抗活性(ANTCL)和毒性(TOXCL)的深度学习模型。从 HMDB 数据库中筛选出 217,345 种化合物,发现了 11 种具有显著 FXR 结合潜力的候选人类代谢物。分子动力学模拟和结合自由能计算显示,与参考化合物 Gly-MCA 相比,五种复合物更为稳定,其中 HMDB0253354(氟维司群)和 HMDB0242367(ZM 189154)的结合自由能更为突出。疏水相互作用,尤其是涉及残基 MET328、PHE329 和 ALA291 的疏水相互作用,有助于提高它们的稳定性。这些结果证明了深度学习在发现 FXR 拮抗剂方面的有效性,并凸显了 HMDB0253354 和 HMDB0242367 作为代谢疾病治疗候选药物的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

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.

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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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