Researchers have leveraged machine learning to create proteins that toggle between two different shapes in response to biological triggers, overcoming a limiting challenge in computational protein design and broadening the potential functionality of designed proteins. Study co-author Banumathi Sankaran, a research scientist in the Molecular Biophysics and Bioimaging Division, used the Advanced Light Source (ALS) beamlines in the Berkeley Center for Structural Biology (BCSB) to validate results with X-ray crystallography data.
Metalloenzymes play an important role in biological systems, including physiology, agriculture, and photosynthesis. Understanding the fundamental chemical mechanism of enzymes is critical for optimizing the biochemical pathways for many aspects of life. In a paper published in Acta Crystallographica Section D, scientists in the Molecular Biophysics and Integrated Bioimaging (MBIB) Division present new computational methods that will enable metalloprotein studies at X-ray Free Electron Laser (XFEL) light sources.