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Data-Mining For Crystal 'gold' at Slac's X-Ray Laser

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Nicholas Sauter, center, a computer staff scientist at Lawrence Berkeley National Laboratory, points to a monitor during an experiment at SLAC's Linac Coherent Light Source X-ray laser.

Researchers at the SLAC National Accelerator Laboratory are analyzing data generated by the lab's Linac Coherent Lightsource using a new data-analytics tools that can produce high-quality images of important proteins using fewer samples.

Credit: Fabricio Sousa/SLAC

SLAC National Accelerator Laboratory researchers are analyzing the data generated from the lab's Linac Coherent Lightsource (LCLS) using Computational Crystallography Toolbox for X-ray Free-electron Lasers (cctbx.xfel), a data-analytics tool that can produce high-quality images of important proteins using fewer samples.

"Such analytical tools might be as important to LCLS experiments as better detectors, sample-delivery systems, and other instruments," says LCLS director Uwe Bergmann.

The researchers say cctbx.xfel should be especially helpful in analyzing proteins that are difficult to crystallize in large quantities for experiments, including many relevant to fighting disease.

The software finds new ways to match LCLS data with Bragg's Law, which describes the mathematics of how X-rays project the molecular blueprints of tiny crystallized samples onto a detector.

"We're trying to really accurately measure the geometry of the detectors--to know where the measurements are being made to the level of microns," says Lawrence Berkeley National Laboratory researcher Nicholas Sauter, who collaborated on the project.

The software also analyzes spots in the X-ray images that other tools reject or overlook, such as streaked, curved, dim, or fuzzy features, increasing the number of usable images.

Berkeley Lab researcher Junko Yano has used the tool to study the molecular machinery at work in photosynthesis.

From SLAC National Accelerator Laboratory
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