A new method for analyzing protein crystals – developed by Cornell researchers and given a funky two-part name – could open up applications for new drug discovery and other areas of biotechnology and biochemistry.
The development, outlined in a paper published March 3 in Nature Communications, provides researchers with the tools to interpret the once-discarded data from X-ray crystallography experiments – an essential method used to study the structures of proteins. This work, which builds on a study released in 2020, could lead to a better understanding of a protein’s movement, structure and overall function.
Protein crystallography produces bright spots, known as Bragg peaks, from the crystals, providing high-resolution information about the shape and structure of a protein. This process also captures blurry images – patterns and clouds related to the movement and vibrations of the proteins – hidden in the background of the Bragg peaks.
These background images are typically discarded, with priority given to the bright Bragg peak imagery that is more easily analyzed.
“We know that this pattern is related to the motion of the atoms of the protein, but we haven’t been able to use that information,” said lead author Steve Meisburger, Ph.D. ’14, a former postdoctoral researcher in the lab of Nozomi Ando, M.S. ’04, Ph.D. ’09, associate professor of chemistry and chemical biology in the College of Arts and Sciences. “The information is there, but we didn’t know how to use it. Now we do.”
Meisburger worked closely with Ando to develop the robust workflow to decode the weak background signals from crystallography experiments called diffuse scattering. This allows researchers to analyze the total scattering from crystals, which depends on both the protein’s structure and the subtle blur of its movements.
Their two-part method – which the team dubbed GOODVIBES and DISCOBALL – simultaneously provides a high-resolution structure of the protein and information on its correlated atomic movements.
GOODVIBES analyzes the X-ray data by separating the movements – subtle vibrations – of the protein from other proteins that might be moving around it. DISCOBALL independently validates these movements for certain proteins directly from the data, allowing researchers to trust the results from GOODVIBES and understand what the protein might be doing.
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Image: Meisburger, Case, & Ando (2020) Nat Commun 11, 1271
