Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Curr Med Chem. 2022;29(5):789-806. doi: 10.2174/0929867328666210910125802.
Protein-ligand interactions are necessary for majority protein functions. Adenosine- 5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is costineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.
蛋白质-配体相互作用是大多数蛋白质功能所必需的。三磷酸腺苷(ATP)就是这样一种配体,它作为辅酶在为细胞活动提供能量、催化生物反应和信号传递方面起着至关重要的作用。了解蛋白质的 ATP 结合残基有助于注释蛋白质功能和药物设计。然而,由于在后基因组时代,大量的蛋白质序列涌入数据库,因此实验确定 ATP 结合残基既昂贵又耗时。为了解决这个问题,已经开发了计算方法来预测 ATP 结合残基。在这篇综述中,我们简要总结了机器学习方法在检测蛋白质 ATP 结合残基中的应用。我们希望这篇综述将有助于进一步的研究。