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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.

Time-resolved serial femtosecond crystallography (TR-SFX) at an x-ray free electron laser enables protein structural changes to be imaged on time-scales from femtoseconds to seconds. It can, however, be difficult to grasp the nature and timescale of global protein motions when structural changes are not isolated near a single active site. New tools are, therefore, needed to represent the global nature of electron density changes and their correlation with modeled protein structural changes. Here, we use TR-SFX data from bacteriorhodopsin to develop and validate a method for quantifying time-dependent electron density changes and correlating them throughout the protein. We define a spherical volume of difference electron density about selected atoms, average separately the positive and negative electron difference densities within each volume, and walk this spherical volume through all atoms within the protein. By correlating the resulting difference electron density amplitudes with time, our approach facilitates an initial assessment of the number and timescale of structural intermediates and highlights quake-like motions on the sub-picosecond timescale. This tool also allows structural models to be compared with experimental data using theoretical difference electron density changes calculated from refined resting and photo-activated structures.