Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis
Category
Published on
Type
journal-article
Author
Cecilia M. Casadei and Ahmad Hosseinizadeh and Gebhard F. X. Schertler and Abbas Ourmazd and Robin Santra
Citation
Casadei, C. M., Hosseinizadeh, A., Schertler, G. F. X., Ourmazd, A., & Santra, R. (2022). Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis. Structural Dynamics, 9(4), 044101. https://doi.org/10.1063/4.0000156
Abstract
Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.
