对接(动物)
蛋白质-配体对接
计算机科学
人工智能
机器学习
人工神经网络
功能(生物学)
药物发现
鉴定(生物学)
蛋白质配体
计算生物学
数据挖掘
虚拟筛选
化学
生物信息学
生物
生物化学
医学
护理部
植物
进化生物学
作者
Xinhao Che,Senchun Chai,Zhongzhou Zhang,Lei Zhang
标识
DOI:10.1016/j.ces.2022.117962
摘要
The identification of the ligand binding sites (LBS) in proteins is of great significance for the elucidation of protein structure and function. Different methods have been published to predict protein–ligand binding sites efficiently. However, most of the current prediction methods only focus on the characteristics of proteins without considering the interactions between proteins and their different ligands, which often leads to frustrating results when the structure or function of the protein is complex or diverse. Therefore, an improved blind docking method with a machine learning-based scoring function is proposed in this paper for the LBS prediction. The blind docking method is used to search binding pockets and an artificial neural network is constructed to analyze binding features, which makes the proposed method possible to distinguish true binding sites from other possible pockets. Two cases of LBS prediction are presented to show the excellent performance of the proposed method. This paper aims to provide new ideas for the prediction of interactions between proteins and small molecules, which can further guide the research of structure-based drug discovery.
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