Niu P H, Ma X J, Wang J
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases/NHC Key Laboratory of Medical Virology and Viral Diseases/National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Changping District, Beijing 102206, China.
Zhonghua Yu Fang Yi Xue Za Zhi. 2025 Jul 6;59(7):1156-1163. doi: 10.3760/cma.j.cn112150-20250117-00051.
The emergence of AlphaFold has catalyzed a paradigm shift in protein structure prediction, redefining the landscape of computational biology through its iterative evolution. The developmental trajectory spans three transformative iterations: the foundational AlphaFold prototype, its revolutionary successor AlphaFold2, and the recently unveiled AlphaFold3. AlphaFold2 marked a quantum leap in 2020 by introducing an end-to-end deep learning architecture that achieved atomic-level accuracy, decisively solving the decades-old protein folding problem as demonstrated by its unprecedented performance at CASP14 (Critical Assessment of Structure Prediction). Building upon this framework, AlphaFold3 represents an evolutionary leap, expanding predictive capabilities to model intricate biomolecular complexes including ligand-protein binding interfaces and nucleic acid interactions.These advancements have unlocked transformative applications across multiple domains: enabling rapid proteome-scale structural annotations in structural biology, accelerating virtual screening pipelines in drug discovery, and facilitating viral protein characterization in emerging virology research. However, persistent limitations in modeling conformational dynamics and transient binding states underscore the need for continued methodological refinement. This comprehensive analysis examines the algorithmic innovations driving AlphaFold's progression, evaluates its multidisciplinary applications, and critically assesses current technical constraints-providing a framework to guide future developments at the intersection of artificial intelligence and molecular bioscience.
AlphaFold的出现催化了蛋白质结构预测的范式转变，通过其迭代式发展重新定义了计算生物学的格局。其发展轨迹跨越了三次变革性迭代：基础的AlphaFold原型、具有革命性的继任者AlphaFold2以及最近推出的AlphaFold3。AlphaFold2在2020年实现了重大飞跃，引入了一种端到端深度学习架构，达到了原子级精度，通过其在第14届蛋白质结构预测关键评估（CASP14）中前所未有的表现，果断解决了存在数十年的蛋白质折叠问题。基于这一框架，AlphaFold3实现了进化上的飞跃，将预测能力扩展到对包括配体-蛋白质结合界面和核酸相互作用在内的复杂生物分子复合物进行建模。这些进展在多个领域开启了变革性应用：在结构生物学中实现快速的蛋白质组规模结构注释，加速药物发现中的虚拟筛选流程，并在新兴病毒学研究中促进病毒蛋白表征。然而，在构象动力学建模和瞬时结合状态方面仍然存在局限性，这突出表明需要持续改进方法。本全面分析考察了推动AlphaFold发展的算法创新，评估了其多学科应用，并批判性地评估了当前的技术限制，提供了一个框架来指导人工智能与分子生物科学交叉领域的未来发展。
