The time revolution in macromolecular crystallography

Georgii Khusainov, Joerg Standfuss, Tobias Weinert
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Abstract

Macromolecular crystallography has historically provided the atomic structures of proteins fundamental to cellular functions. However, the advent of cryo-electron microscopy for structure determination of large and increasingly smaller and flexible proteins signaled a paradigm shift in structural biology. The extensive structural and sequence data from crystallography and advanced sequencing techniques have been pivotal for training computational models for accurate structure prediction, unveiling the general fold of most proteins. Here, we present a perspective on the rise of time-resolved crystallography as the new frontier of macromolecular structure determination. We trace the evolution from the pioneering time-resolved crystallography methods to modern serial crystallography, highlighting the synergy between rapid detection technologies and state-of-the-art x-ray sources. These innovations are redefining our exploration of protein dynamics, with high-resolution crystallography uniquely positioned to elucidate rapid dynamic processes at ambient temperatures, thus deepening our understanding of protein functionality. We propose that the integration of dynamic structural data with machine learning advancements will unlock predictive capabilities for protein kinetics, revolutionizing dynamics like macromolecular crystallography revolutionized structural biology.
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大分子晶体学的时间革命
大分子晶体学历来提供对细胞功能至关重要的蛋白质原子结构。然而,冷冻电镜技术的出现标志着结构生物学范式的转变,该技术可用于确定大型蛋白质以及越来越小和越来越灵活的蛋白质的结构。来自晶体学和先进测序技术的大量结构和序列数据对于训练精确结构预测的计算模型至关重要,揭示了大多数蛋白质的一般折叠。在此,我们从时间分辨晶体学作为大分子结构测定新前沿的崛起角度进行了阐述。我们追溯了从开创性的时间分辨晶体学方法到现代序列晶体学的演变过程,强调了快速检测技术与最先进的 X 射线源之间的协同作用。这些创新正在重新定义我们对蛋白质动态的探索,高分辨率晶体学在阐明常温下的快速动态过程方面具有得天独厚的优势,从而加深了我们对蛋白质功能的理解。我们认为,将动态结构数据与机器学习的进步相结合,将开启蛋白质动力学的预测能力,从而彻底改变动力学,就像大分子晶体学彻底改变结构生物学一样。
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