A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Chuqi Lei, Senbiao Fang, Yaohang Li, Fei Guo, Min Li
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引用次数: 0

Abstract

background: computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods treat the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pock-et residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. method: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. result: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. conclusion: Our analysis confirms that the screening performance of the various number of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.
A-RFP:基于同源蛋白质的自适应残基柔性预测方法,用于改善蛋白质配体对接
背景:计算分子对接在确定受体-配体的精确构象方面发挥着重要作用,成为药物发现的有力工具。在过去的 30 年中,大多数计算对接方法都将受体结构视为刚体,尽管柔性对接通常能获得更高的精确度。柔性对接的主要缺点是计算成本较高。由于不同的蛋白质受体残基表现出不同程度的柔性,半柔性对接方法在刚性对接和柔性对接之间取得了平衡,成功地以相对较低的计算成本预测了高精度的构象:在我们的研究中,通过定量分析评估了柔性口袋残基的数量,并提出了一种名为 A-RFP 的新型自适应残基柔性预测方法,以提高对接性能。在同源信息的基础上,结合 RMSD、残基侧链与配体之间的距离以及侧链方向,采用联合策略预测口袋残基的柔性。对于每一对受体配体,A-RFP 都能提供一个具有最佳亲和力的对接构象:通过分析 3507 对目标物-配体在 5 个从 0 到 10 的不同数值范围内的对接亲和力,我们发现一个普遍的趋势是,柔性残基的数量越多,使用 Autodock Vina 不可避免地会改善对接结果。但是,仍然存在一定数量的反例。为了验证 A-RFP 的有效性,实验评估在 5 个蛋白质的小规模虚拟筛选中进行了测试,结果证实 A-RFP 可以提高对接性能。在一个包含 85 个受体的低相似性数据集上进行的柔性受体虚拟筛选验证了残基柔性综合评估的准确性。此外,我们还研究了三种与 FDA 批准药物配伍的受体,这进一步证明了 A-RFP 在配体发现中可以发挥合适的作用:我们的分析证实,不同数量的柔性残基在不同受体上的筛选性能差异很大。这表明细粒度对接方法可以弥补上述不足。因此,我们提出了基于同源信息的自适应口袋残基柔性预测方法 A-RFP。在不考虑计算资源和时间成本的情况下,A-RFP 提供了最佳的对接结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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