Scaling and merging time-resolved pink-beam diffraction with variational inference

Area of Science:

• Structural Biology
• Biophysics
• Computational Chemistry

Background:

• Time-resolved x-ray crystallography (TR-X) enables atomic-resolution observation of molecular dynamics.
• Analyzing TR-X data is challenging due to difficulties in extracting subtle, time-dependent signals.
• Variational inference (VI) has shown promise in merging redundant observations to overcome these analytical bottlenecks.

Purpose of the Study:

• To present a successful application of variational inference (VI) for analyzing time-resolved x-ray diffraction data.
• To demonstrate a strategy for extracting high signal-to-noise electron density changes from enzyme dynamics.
• To provide a practical case study for researchers using VI in time-resolved crystallography.

Main Methods:

• Application of variational inference (VI) to time-resolved x-ray crystallography data.
• Analysis of enzyme DJ-1 dynamics upon reaction with methylglyoxal.
• An ablation study to assess the impact of hyperparameter choices on VI model performance.

Main Results:

• Successful extraction of time-dependent changes in electron density for the enzyme DJ-1.
• Demonstration of a robust strategy for enhancing signal-to-noise ratio in TR-X data.
• Quantification of the influence of individual hyperparameters on the VI model's effectiveness.

Conclusions:

• Variational inference provides an effective statistical approach for analyzing challenging time-resolved x-ray crystallography data.
• The presented strategy facilitates the extraction of high-quality dynamic information from molecular reactions.
• This case study serves as a guide for implementing VI in structural dynamics research.