Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States.
Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology and Information Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China.
J Chem Inf Model. 2023 Mar 13;63(5):1413-1428. doi: 10.1021/acs.jcim.2c01634. Epub 2023 Feb 24.
Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.
变构机制是蛋白质用于协调复杂生化过程和控制细胞内通讯的常用调节工具。变构分子事件的定量理解和特征描述是现代生物学的主要挑战之一，需要整合创新的计算实验方法，以获得变构状态、相互作用和动态构象景观的原子水平知识。新兴人工智能 (AI) 技术推动的计算和实验研究不断发展，为从第一性原理探索和学习蛋白质变构的宇宙开辟了新的范例。在这篇综述中，我们分析了变构蛋白功能高通量深度突变扫描的最新进展；Alpha-fold 结构预测方法在研究蛋白质动力学和变构中的应用和最新适应；机器学习和增强采样技术在变构特征描述中的新前沿；以及用于研究变构系统的结构生物学方法的最新进展。我们还强调了最近关于 SARS-CoV-2 刺突 (S) 蛋白的计算和实验研究，揭示了变构调节驱动功能构象变化、与宿主受体结合相互作用以及 S 蛋白突变逃逸机制的重要且常常隐藏的作用，这些对于病毒感染至关重要。我们以总结和展望未来的方向结束，这表明 AI 增强的生物物理和计算机模拟方法开始将蛋白质变构的研究转变为变构景观、隐藏的变构状态和机制的系统特征描述，这可能会给分子生物学和药物发现带来新的革命。
