Scientists for the first time have viewed how bacterial proteins self-assemble into thin sheets and begin to form the walls of the outer shell for nano-sized polyhedral compartments that function as specialized factories. Researchers in the Molecular Biophysics & Integrated Bioimaging Division determined the 3-D structure of the basic building block protein from crystallized samples in the Berkeley Center for Structural Biology at the Advanced Light Source. Their findings may eventually help improve drug delivery systems. Read more in the Berkeley Lab News Center.
A diode beamstop technology for real time X-ray beam intensity measurement, developed by Diane Bryant and Simon Morton at the Berkeley Center for Structural Biology, was licensed to MiTeGen, which will commercialize a product to enhance X-ray beamlines used for pharma, materials, and biotech research. The Innovation and Partnerships Office managed the licensing process. Read more at IPO.
Mina Bissell, Curt Hines, and Irene Kuhn of the Biological Systems & Engineering Division led the development of the first clinically-relevant mouse model of human breast cancer to successfully express functional estrogen receptor positive adenocarcinomas. This model should be a powerful tool for testing therapies for aggressive ER+ breast cancers and for studying luminal cancers. Read more at the Berkeley Lab News Center.
Scientists working at the Berkeley Center for Structural Biology in the Advanced Light Source (ALS) recently solved the crystallographic structures of several amine transporters in an effort to better understand why the human brain responds to chemicals like dopamine and serotonin. Their findings will help design drugs to treat neurological diseases and may also lead to a better understanding of how drug addiction can be managed. The work was led by HHMI Investigator Eric Gouaux of the Oregon Health & Science University. Read the ALS Science Highlight.
Deep learning is not a new concept in academic circles or behind the scenes at “Big Data” companies like Google and Facebook, where algorithms for automated pattern recognition are a fundamental part of the infrastructure. A collaborative effort at Berkeley Lab is working to apply deep learning software tools developed for high performance computing environments to a number of “grand challenge” science problems running computations at the National Energy Research Scientific Computing Center (NERSC) and other supercomputing facilities. Researchers in Berkeley Lab’s Biological Systems and Engineering Division, including Kris Bouchard, are using a deep learning library to analyze recordings of the human brain during speech production. More information about deep learning at NERSC.