A recent study published in Nature Plants used a combination of genetic mutation and X-ray crystallography, conducted at the Berkeley Center for Structural Biology, to reveal structural details of a key enzyme involved in plant signaling.
Researchers from the Baylor College of Medicine employed previously constructed DNA-encoded chemistry technology (DEC-tec) libraries to identify several candidate molecules that could inhibit the action of Mpro, the main protease of SARS-CoV-2. In a recent study, the researchers described CDD-1713, a new inhibitor to the enzyme Mpro that is involved in propagating the virus. The X-ray crystallographic data, which was collected by Banumathi Sankaran in the Molecular Biophysics and Integrated Bioimaging Division, allowed the researchers to determine that CDD-1713 inhibits the activity of Mpro by binding in the active site of this enzyme.
For structural biologists who study proteins, predicting their shape offers a key to understanding their function and accelerating treatments for diseases like cancer and COVID-19. The current approaches to accurately mapping that shape have their limitations, but by applying powerful machine learning methods to the large library of protein structures it is now possible to predict a protein’s shape from its gene sequence.
Elliot Perryman, a computer science and physics major at the University of Tennessee, began working with staff scientist Peter Zwart in the Center for Advanced Mathematics for Energy Research Applications (CAMERA) last fall through the Berkeley Lab Undergraduate Research (BLUR) program. Together they developed an algorithm that will extract better structures from low-quality crystallographic diffraction data.
In recent years, cryo-electron microscopy (cryo-EM) technology has advanced to the point that it can produce structures with atomic-level resolution for many types of molecules. Yet in some situations, even the most sophisticated cryo-EM methods still generate maps with lower resolution and greater uncertainty than required to tease out the details of complex chemical reactions.
In a study published in Nature Methods, a multi-institutional team led by Tom Terwilliger from the New Mexico Consortium and including researchers from Berkeley Lab demonstrates how a new computer algorithm improves the quality of the 3D molecular structure maps generated with cryo-EM.