Category: Science Highlights

Structure–Function Relationships in Sequence-Controlled Copolymers for Rare Earth Element Chelation

In this publication led by Abigail Knight, an Assistant Professor of Chemistry at UNC-Chapel Hill and a SIBYLS SAXS user, the authors report on systematic research into amphiphilic polymer chelators, specifically examining how composition and patterning influence binding affinity and selectivity for rare earth elements (REEs). Using complementary dynamic light scattering (DLS) and small-angle X-ray…
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FlbB forms ring for assembly and motility

For this publication, the authors utilized SEC-SAXS data collected at SIBYLS beamline 12.3.1, along with SAXS model fitting, to confirm and further analyze the proteins of interest.  Beamline scientist Michal Hammel and Postdoc Joshua Del Mundo played a vital role in the research. This work provides crucial mechanistic insights into spirochete-specific adaptations and their roles…
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Predicting RNA structure and dynamics with deep learning and solution scattering

The use of deep-learning and statistical methods plays a significant role in the prediction of accurate structure and atomistics RNA models.  In this paper, the authors describe new a deep-learning tool they developed called Scoper, which is capable of using experimental SAXS data from the SIBYLS beam line to determine the most likely 3-dimensional conformation of…
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SIBYLS research in Bifurcation of Electrons is highlighted in ALS Science Briefs newsletter

Scientists at SIBYLS used small angle x-ray scattering to understand a microbial protein involved in the bifurcation of high and low energy electrons in microbial metabolism. By analyzing the SAXS data, the SIBYLS team was able to find that NADH activates the protein’s mechanics, allowing it to act as the wheel and ropes of a…
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SIBYLS help signal target for Anticancer Drugs

Researchers from Genentech in collaboration with SIBYLS beamline scientist, Michal Hammel, used Small Angle X-Ray scattering to learn how an assembly of three proteins works together to transmit signals for cell division. The work reveals new targets for the development of drugs to fight certain types of cancer, including lung, colorectal, and pancreatic.

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Sample Centering with Google’s AutoML

SIBYLS beamline scientist, Scott Classen, collaborated with ScienceIT consultants Shawfeng Dong and Fengchen Liu to use AutoML machine learning for the LoopDHS project. As part of the effort, IT student intern Jordan Jung developed a training dataset for the model to improve the accuracy. These developments enabled Dr. Classen to develop loopDHS a custom software…
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SIBYLS makes cover of Antibody Therapeutics

Analyzing SAXS data collected at SIBYLS beamline 12.3.1, Michal Hammel determined the structural arrangement of the VH and VL domains in the COBRA™ (COnditional Bispecific Redirected Activation) T-cell engagers. The study showed that the structural arrangement of the domains in COBRA is different from the expected yet the stable fold is maintained. Findings provide insights…
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Study finds ‘Missing Link’ in the Evolutionary History of Carbon-fixing Protein Rubisco

SIBYLS beamline scientists contribute to the discovery of an ancient form of rubisco, the most abundant enzyme on earth and critical to life as we know it. By analyzing SEC-SAXS data collected at 12.3.1, they were able to capture how the enzyme’s structure changes during different states of activity.      READ MORE

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