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Abstract Sample consumption for serial femtosecond crystallography (SFX) with X-ray free electron lasers (XFELs) remains a major limitation preventing broader use of this powerful technology in macromolecular crystallography. This drawback is exacerbated in the case of time-resolved (TR)-SFX experiments, where the amount of sample required per reaction time point is multiplied by the number of time points investigated. Thus, in order to reduce the limitation of sample consumption, here we demonstrate the implementation of segmented droplet generation in conjunction with a mix-and-inject approach for TR studies on NAD(P)H:quinone oxidoreductase 1 (NQO1). We present the design and application of mix-and-inject segmented droplet injectors for the Single Particles, Clusters, and Biomolecules & Serial Femtosecond Crystallography (SPB/SFX) instrument at the European XFEL (EuXFEL) with a synchronized droplet injection approach that allows liquid phase protein crystal injection. We carried out TR-crystallography experiments with this approach for a 305 ms and a 1190 ms time point in the reaction of NQO1 with its coenzyme NADH. With this successful TR-SFX approach, up to 97% of the sample has been conserved compared to continuous crystal suspension injection with a gas dynamic virtual nozzle. Furthermore, the obtained structural information for the reaction of NQO1 with NADH is an important part of the future elucidation of the reaction mechanism of this crucial therapeutic enzyme.
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Filling data analysis gaps in time-resolved crystallography by machine learning
There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.
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- Award ID(s):
- 1231306
- PAR ID:
- 10567363
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Structural Dynamics
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2329-7778
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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