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作 者:宫维斌 GONG Wei-Bin(National Laboratory of Biomacromolecules,Institute of Biophysics,Chinese Academy of Sciences,Beijing 100101,China)
机构地区:[1]中国科学院生物物理研究所,生物大分子重点实验室,北京100101
出 处:《生物化学与生物物理进展》2024年第12期3073-3083,共11页Progress In Biochemistry and Biophysics
基 金:国家自然科学基金(32371273)资助项目。
摘 要:近年来,基于深度学习的方法研究在蛋白质结构预测领域实现了重大突破。AlphaFold 2 (AF2)于2021年开源发布,实现了蛋白质暨蛋白质复合物三维结构的高精度预测,使得研究人员能够快速获取可靠的三维结构信息,显著加速了蛋白质结构与功能研究的进展。2024年发布的AlphaFold 3 (AF3)更进一步,能对蛋白质-核酸、蛋白质-小分子等生物复合物的三维结构进行精准预测。AF3采用改进的算法与更高效的模型,大幅提升了预测准确度,特别是在抗原-抗体复合物、蛋白质-小分子复合物等方面展现出卓越的性能。AlphaFold的成功不仅为结构生物学带来了革命性进展,还在药物研发、蛋白质设计、分子功能机制研究等领域展示了巨大的应用潜力,推动了生物医学研究的革新。本文将回顾AlphaFold及相关蛋白质结构预测方法的研发历史,概述其关键技术和当下应用,并结合其局限性,展望未来的研究方向和应用。In recent years,deep learning-based methods have achieved significant breakthroughs in protein structure prediction.The open-source release of AlphaFold 2(AF2)in 2021 enabled high-precision prediction of three-dimensional structures for both individual proteins and protein complexes,allowing researchers to rapidly obtain reliable structural information and greatly accelerating advancements in protein structure and function studies.The release of AlphaFold 3(AF3)in 2024 took this further by achieving accurate predictions of three-dimensional structures for protein-nucleic acid and protein-small molecule complexes.With improved algorithms and a more efficient model,AF3 significantly enhanced prediction accuracy,especially demonstrating outstanding performance in antigen-antibody and protein-small molecule complexes.The success of AlphaFold has not only brought revolutionary progress to structural biology but also showcased immense application potential in fields such as drug development,protein design,and molecular function research,driving innovation in biomedical studies.This article will review the development history of AlphaFold and related protein structure prediction methods,summarize their key technologies and current applications,and,by considering their limitations,provide an outlook on future research directions and applications.
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