人工智能时代下的蛋白质从头设计  

De novo protein design in the age of artificial intelligence

作　　者：刘南 金小程 杨崇周 王梓洋 闵小平[2] 葛胜祥[1] LIU Nan;JIN Xiaocheng;YANG Chongzhou;WANG Ziyang;MIN Xiaoping;GE Shengxiang(State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics,School of Public Health,Xiamen University,Xiamen 361005,Fujian,China;Institute of Artificial Intelligence,School of Informatics,Xiamen University,Xiamen 361005,Fujian,China)

机构地区：[1]厦门大学公共卫生学院分子疫苗学与分子诊断国家重点实验室,福建厦门361005 [2]厦门大学信息学院人工智能研究院,福建厦门361005

出　　处：《生物工程学报》2024年第11期3912-3929,共18页Chinese Journal of Biotechnology

基　　金：国家自然科学基金(62272399);医学科学院医学科学创新基金(2019RU022);中国福建省重点项目基金(2021J02006);中央高校基本科研业务费(20720220005,20720220006)。

摘　　要：具有特定功能和特性的蛋白质在生物医药、纳米材料等领域至关重要。蛋白质从头设计能够定制序列以生成具有所需结构、自然界中未存在的蛋白质。近年来,随着人工智能的迅猛发展,深度学习生成模型逐渐成为强大工具,许多功能性蛋白质的设计都达到了原子级别的精度。本文概述了蛋白质从头设计的演进,着重介绍了其最新算法模型,并分析了其存在的问题,如设计成功率低、精度不足以及对实验验证的依赖性,最后探讨了蛋白质设计的未来趋势,旨在为研究者和从业者提供有益参考。Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology.De novo protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature.In recent years,with the rapid development of artificial intelligence(AI),deep learning-based generative models have increasingly become powerful tools,enabling the design of functional proteins with atomic-level precision.This article provides an overview of the evolution of de novo protein design,with focus on the latest algorithmic models,and then analyzes existing challenges such as low design success rates,insufficient accuracy,and dependence on experimental validation.Furthermore,this article discusses the future trends in protein design,aiming to provide insights for researchers and practitioners in this field.

关 键 词：蛋白质从头设计 人工智能 深度学习 扩散模型 

分 类 号：TQ936[化学工程] TP18[自动化与计算机技术—控制理论与控制工程]