Visualizing Reaction Pathways via Reciprocal Space Kinetic Decomposition

Abstract

Time-resolved serial crystallography (TR-SX) has emerged as a powerful method for capturing ultrafast structural dynamics in proteins. TR-SX continues to produce remarkable studies, revealing previously unobserved transient states and providing deeper insights into processes such as drug targeting, DNA repair, and photosynthesis. However, extracting weak structural signals from noisy time-resolved datasets remains a major challenge. Robust computational methods are therefore required to isolate the signals associated with the underlying transient states. Importantly, this should be performed in reciprocal space to preserve compatibility with established downstream structure refinement workflows. Here, we introduce a framework for kinetic decomposition directly in reciprocal space that enables separation of kinetically distinct structural states. The method decomposes crystallographic data according to a predefined kinetic model, improving the recovery of weak transient signals and enhancing mechanistic interpretation from limited time-resolved datasets. We validate the framework using simulated data based on a previously published time-resolved crystallography study and demonstrate its application to a new TR-SX dataset comprising 17 time points. We show that the method separates the reciprocal space signatures of four intermediates by incorporating kinetic information from a predefined reaction model. This establishes a workflow for extracting kinetic states directly from time-resolved X-ray diffraction data that can be seamlessly integrated into existing crystallographic structure-determination pipelines.