Zhao Jingtian, Cao Yang, Zhang Le
College of Computer Science, Sichuan University, Chengdu 610065, China.
Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China.
Comput Struct Biotechnol J. 2020 Feb 17;18:417-426. doi: 10.1016/j.csbj.2020.02.008. eCollection 2020.
Proteins participate in various essential processes via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.
蛋白质通过与其他分子的相互作用参与各种重要过程。识别参与这些相互作用的残基不仅为蛋白质功能研究提供生物学见解，而且对药物发现也具有重要意义。因此，预测蛋白质-配体结合位点长期以来一直是生物信息学和计算机辅助药物发现领域的研究热点。在这篇综述中，我们首先介绍预测蛋白质-配体结合位点的研究背景，然后将方法分为四类，即基于三维结构的方法、基于模板相似性的方法、基于传统机器学习的方法和基于深度学习的方法。我们描述了每一类中的代表性算法，并更详细地阐述了基于机器学习和深度学习的预测方法。最后，我们讨论了当前研究的趋势和挑战，如基于分子动力学模拟的隐秘结合位点预测，并突出了近期的前瞻性方向。
