Scaling and Merging Time-Resolved Laue Data with Variational Inference

Zielinski, K. A.; Dolamore, C.; Wang, H. K.; Henning, R. W.; Wilson, M. A.; Pollack, L.; Srajer, V.; Hekstra, D. R.; Dalton, K. M.

doi:10.1101/2024.07.30.605871

bioRxiv · 10.1101/2024.07.30.605871

Scaling and Merging Time-Resolved Laue Data with Variational Inference

Zielinski, K. A.·Dolamore, C.·Wang, H. K.·Henning, R. W.·Wilson, M. A.·Pollack, L.·Srajer, V.·Hekstra, D. R.·Dalton, K. M.

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Abstract

Time-resolved X-ray crystallography (TR-X) at synchrotrons and free electron lasers is a promising technique for recording dynamics of molecules at atomic resolution. While experimental methods for TR-X have proliferated and matured, data analysis is often difficult. Extracting small, time-dependent changes in signal is frequently a bottleneck for practitioners. Recent work demonstrated this challenge can be addressed when merging redundant observations by a statistical technique known as variational inference (VI). However, the variational approach to time-resolved data analysis requires identification of successful hyperparameters in order to optimally extract signal. In this case study, we present a successful application of VI to time-resolved changes in an enzyme, DJ-1, upon mixing with a substrate molecule, methylglyoxal. We present a strategy to extract high signal-to-noise changes in electron density from these data. Furthermore, we conduct an ablation study, in which we systematically remove one hyperparameter at a time to demonstrate the impact of each hyperparameter choice on the success of our model. We expect this case study will serve as a practical example for how others may deploy VI in order to analyze their time-resolved diffraction data.

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Zielinski, K. A., Dolamore, C., Wang, H. K., Henning, R. W., Wilson, M. A., Pollack, L., Srajer, V., Hekstra, D. R., Dalton, K. M.. 2024-07-31. Scaling and Merging Time-Resolved Laue Data with Variational Inference. https://doi.org/10.1101/2024.07.30.605871

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