College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816, Jiangsu, People's Republic of China.
BMC Bioinformatics. 2022 Nov 2;23(1):456. doi: 10.1186/s12859-022-04995-2.
Ligand-protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets.
In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods.
MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .
配体-蛋白质相互作用在定义蛋白质功能方面起着关键作用，因此检测给定蛋白质的天然配体是一项非常重要的生物工程任务。特别是，随着基于人工智能的结构预测算法的快速发展，可以以低成本获得高可靠性和高精度的批量结构模型，这就迫切需要基于蛋白质结构预测天然配体。近年来，尽管已经开发了几种基于结构的方法来预测配体结合口袋和配体结合位点，但仍然缺乏准确和快速的方法，特别是对于配体结合区域和口袋中配体的空间延伸的预测。
在本文中，我们提出了一种仅基于蛋白质结构预测配体结合区域的多层动力学扰动分析（MDPA）方法，这是我们之前开发的快速动态扰动分析（FDPA）方法的扩展。在 MDPA/FDPA 中，配体结合往往发生在导致蛋白质构象动力学发生较大变化的区域。使用配体-蛋白质复合物的标准验证数据集检验 MDPA，得到的平均配体结合位点预测 Matthews 系数为 0.40，至少有 71%的情况下预测精度至少为 50%。特别是，在 80%的情况下，预测的配体结合区域与天然配体重叠至少 50%。该方法还与其他最先进的基于结构的方法进行了比较。
MDPA 是一种基于结构的方法，用于检测蛋白质表面的配体结合区域。我们的计算表明，蛋白质口袋内的一系列空间与蛋白质有细微的相互作用，这会显著影响蛋白质的整体动力学。这项工作为进一步的对接和分析方法提供了一个有价值的工具，可用于蛋白质功能注释中的天然配体检测。MDPA 方法的源代码可在以下网址免费获取：https://github.com/mingdengming/mdpa。
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