Abstract：
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.
摘要：
蛋白质-蛋白质相互作用参与活细胞中的几乎所有过程，并决定蛋白质的生物学功能。为了获得对蛋白质-蛋白质相互作用的机械理解，蛋白质复合物的三级结构已经通过生物物理实验方法确定，如X射线晶体学和低温电子显微镜。然而,由于实验方法资源昂贵，已经开发了许多计算方法来模拟蛋白质复合物结构。计算蛋白质复合物建模（蛋白质对接）的困难之一是在通常由对接方法生成的许多模型中选择最准确的模型。本文综述了蛋白质对接模型评估方法的研究进展，重点关注将深度学习应用于几种网络体系结构的最新发展。
