Protein-protein interaction prediction with deep learning: A comprehensive review.

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein–protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein–protein interaction and protein–ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein–protein interaction and their sites, protein–ligand binding, and protein design.

大多数蛋白质通过自身相互作用或与其他分子相互作用来执行其生物学功能。因此，通过识别蛋白质 - 蛋白质相互作用（PPI），人们可以获得有关蛋白质功能、疾病流行情况和治疗开发的生物学见解。然而，由于蛋白质的多样性，通过实验方法寻找相互作用和非相互作用的蛋白质对是劳动密集型且耗时的。因此，蛋白质 - 蛋白质相互作用和蛋白质 - 配体结合问题在生物信息学和计算机辅助药物发现领域引起了关注。深度学习方法为科学家从基因组预测蛋白质的三维结构、预测蛋白质的功能和属性以及修改和设计具有所需功能的新蛋白质铺平了道路。本综述重点关注应用于预测蛋白质功能、蛋白质 - 蛋白质相互作用及其位点、蛋白质 - 配体结合和蛋白质设计等问题的近期深度学习方法。