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$Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography$

Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

Dynamic X-ray diffraction sampling for protein crystal positioning.

Nicole M Scarborough¹, G M Dilshan P Godaliyadda², Dong Hye Ye²

¹Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.

Journal of Synchrotron Radiation

|December 24, 2016

Summary

This summary is machine-generated.

A new algorithm, sparse supervised learning for dynamic sampling (SLADS), significantly reduces X-ray dose for protein crystal positioning. This method intelligently selects measurement points, minimizing crystal damage and improving diffraction imaging efficiency.

Keywords:

X-ray diffraction dynamic sampling nonlinear optical microscopy second-harmonic generation supervised learning approach two-photon-excited fluorescence

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$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Area of Science:

Crystallography
Biophysics
Machine Learning

Background:

Crystal centering is crucial for macromolecular diffraction at synchrotrons.
X-ray diffraction mapping aids crystal localization but increases radiation dose.
High X-ray exposure can damage crystals before data collection.

Purpose of the Study:

To develop a dose-reduction strategy for diffraction-based protein crystal positioning.
To implement a sparse supervised learning approach for dynamic sampling (SLADS).
To minimize X-ray exposure and crystal damage during positioning and imaging.

Main Methods:

SLADS algorithm for single-pixel measurements, selecting optimal sampling locations.
Utilizing ground-truth diffraction data and in situ two-photon-excited fluorescence microscopy.
Implementation at a synchrotron beamline (Argonne National Laboratory).

Main Results:

SLADS achieved image reconstruction with significantly reduced sampling (3-31% of total volume).
Substantial reduction in X-ray dose to protein crystals was demonstrated.
Effective crystal positioning and image reconstruction were achieved even without ground-truth data.

Conclusions:

SLADS offers a powerful method for dose reduction in X-ray diffraction imaging.
The algorithm minimizes radiation damage, preserving crystal integrity for data collection.
SLADS integration can enhance efficiency and data quality in macromolecular crystallography.