摘要
蛋白质功能与其三维结构间存在着密不可分的关联,这一认知长期引领着生命科学领域的探索方向。科学家们为解析蛋白质结构投入了大量精力,而蛋白质测序技术的迅猛发展,使得序列数据呈指数级增长,与结构研究进展之间的差距日益显著。过去十几年间,人工智能技术的蓬勃发展为这一困境带来了转机,其以深度学习、神经网络等核心算法为支撑,推动蛋白质工程迎来了全新变革。借助人工智能技术,新一代蛋白质结构预测和设计方法取得重大突破。这些基于先进算法的工具,极大地提高了蛋白质结构建模的准确性和速度。它们不仅助力结构生物学、药物研发等领域的发展,还为蛋白质合成提供了关键依据。除此之外,人工智能正推动蛋白质研究从“结构解析”向“逆向设计”转型。通过构建序列-结构-功能的多维度关联模型,研究人员能够基于特定功能需求,反向设计具有预期结构的蛋白质序列。从而更精准地设计蛋白质序列,为生物合成开辟新路径。本综述聚焦于人工智能在蛋白质工程中的核心作用,阐述了蛋白质工程目前所面临的挑战和传统蛋白结构解析方法所面临的瓶颈,并以此引入介绍了基于人工智能的结构预测工具的发展,分析其在蛋白质合成中的应用;探讨人工智能驱动下,从结构解析到合成蛋白的算法革命及未来潜在方向,以期为该领域的研究提供参考。
The intricate relationship between protein function and its three-dimensional structure has long been a fundamental guiding principle in life sciences research.While scientists have dedicated substantial effort to deciphering protein structures,the exponential growth in sequence data fueled by rapid advances in protein sequencing technologies has significantly outpaced progress in structural studies.Over the past decade,the burgeoning field of artificial intelligence(AI),underpinned by core algorithms such as deep learning and neural networks,has emerged as a transformative force in protein engineering,offering new avenues to address this disparity.Leveraging AI,next-generation methods for protein structure prediction and design have achieved remarkable breakthroughs.These advanced algorithm-based tools have dramatically enhanced both the accuracy and speed of protein structure modeling.They are not only accelerating progress in structural biology and drug discovery but also providing crucial foundations for protein synthesis.Furthermore,AI is catalyzing a paradigm shift in protein research,moving beyond‘structure determination’towards‘inverse design’.By constructing multidimensional models that elucidate sequencestructure-function relationships,researchers can now reverse-engineer protein sequences with desired structural characteristics based on specific functional requirements. This capability for precise protein sequence design is paving new pathways for biosynthetic applications.This review focuses on the pivotal role of AI in protein engineering. Firstly, it outlines the current challenges in the protein engineering and the bottlenecks in traditional protein structure determination methods. Then it introduces the development of AI-based structure prediction tools, followed by an analysis of their application in protein synthesis. Finally, it then explores the algorithm-driven revolution facilitating the transition from structure determination to de novo protein synthesis, and discusses potential future directions, aiming to provide a reference framework for ongoing research in this field.
作者
蔡如凤
杨宇轩
于基正
李佳楠
CAI Ru-feng;YANG Yu-xuan;YU Ji-zheng;LI Jia-nan(College of Life Science,Jianghan University,Wuhan 430056)
出处
《生物技术通报》
北大核心
2025年第8期1-10,共10页
Biotechnology Bulletin
基金
湖北省重点研发项目(2022BBA064)。
关键词
人工智能
蛋白质工程
蛋白质结构预测
蛋白质设计
生物合成
artificial intelligence
protein engineering
prediction of protein structure
protein design
biosynthesis
