Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

化学 配体(生物化学) 核受体 兴奋剂 受体 分子模型 计算生物学 对接(动物) 立体化学 分子 敌手 结合位点 生物物理学 生物化学 转录因子 生物 医学 基因 护理部 有机化学
作者
Qinghua Wang,Zhe Wang,Sheng Tian,Lingling Wang,Rongfan Tang,Yang Yu,Jingxuan Ge,Tingjun Hou,Haiping Hao,Huiyong Sun
出处
期刊:Journal of Chemical Information and Modeling [American Chemical Society]
卷期号:62 (17): 3993-4007 被引量:9
标识
DOI:10.1021/acs.jcim.2c00851
摘要

The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.
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