Meng Yajie, Zhang Zhuang, Zhou Chang, Tang Xianfang, Hu Xinrong, Tian Geng, Yang Jialiang, Yao Yuhua
College of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China.
Geneis Beijing Co, Beijing, China.
Front Pharmacol. 2025 Apr 3;16:1498662. doi: 10.3389/fphar.2025.1498662. eCollection 2025.
The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.
深度学习算法在蛋白质结构预测中的应用极大地影响了药物发现与开发。准确的蛋白质结构对于理解生物过程和设计有效的治疗方法至关重要。传统上，诸如X射线晶体学、核磁共振和冷冻电子显微镜等实验方法一直是确定蛋白质结构的金标准。然而，这些方法通常成本高昂、效率低下且耗时。与此同时，已知蛋白质序列的数量远远超过通过实验确定结构的数量，这就产生了一个缺口，使得有必要使用计算方法。在过去十年中，深度学习已成为应对这一挑战的一种很有前景的解决方案。本综述提供了在蛋白质结构预测中应用深度学习方法和工具的全面指南。我们首先概述与蛋白质结构预测相关的数据库，然后深入探讨最近开发的大语言模型以及基于深度学习的最先进方法。综述最后展望了蛋白质结构预测的未来，强调了潜在的挑战和机遇。
