变构机制是蛋白质常用的调节工具,用于协调复杂的生化过程并控制细胞中的通讯。变构分子事件的定量理解和表征是现代生物学中的主要挑战之一,需要集成创新的计算实验方法来获得原子级的变构状态知识。互动,和动态构象景观。新兴的人工智能(AI)技术赋予了越来越多的计算和实验研究,为从第一原理探索和学习蛋白质变构宇宙开辟了新的范式。在这篇综述中,我们分析了变构蛋白功能的高通量深度突变扫描的最新进展;Alpha-fold结构预测方法在蛋白质动力学和变性反应研究中的应用和最新适应;集成机器学习和增强采样技术以表征变性反应的新领域;以及变性反应系统研究的结构生物学方法的最新进展。我们还重点介绍了SARS-CoV-2尖峰(S)蛋白的最新计算和实验研究,揭示了驱动功能构象变化的变构调节的重要且通常隐藏的作用。与宿主受体的结合相互作用,以及对病毒感染至关重要的S蛋白的突变逃逸机制。最后,我们对未来方向进行了总结和展望,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的系统表征,隐藏的变构状态,以及可能带来分子生物学和药物发现新革命的机制。
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.