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.
Machine Learning Helps Link Chemical Exposure and Obesity
Scientists at Berkeley Lab and their collaborators developed a machine learning technique to discover obesity-related mixed chemical exposure patterns associated with environmental health risk in the general U.S. population. To assess this, they used indicators like body mass index and waist circumference.
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