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Adams Appointed ALD for Biosciences

January 31, 2023

Paul Adams

Berkeley Lab Director Mike Witherell has appointed Paul Adams to the position of Associate Lab Director (ALD) for Biosciences. Adams has been serving as the interim ALD for the Biosciences Area since 2021, taking over for Mary Maxon, who is now with Schmidt Futures. Over the past two decades, Adams has played an important role promoting Berkeley Lab’s leadership in structural biology nationally and internationally. As ALD, Adams will continue to advance the Area’s scientific vision and operations excellence while advancing IDEA principles and broadening outreach to institutions serving groups that are underrepresented in STEM.

Enzyme Structure Reveals Key Details in Strigolactone Signaling

June 10, 2022

Figure from original publication depicting crystal structure of D3/MAX2

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.

BCSB Determines Interactions of Potential Inhibitor with SARS-CoV-2 Protease

November 1, 2021

color-enhanced image of SARS-CoV-2 taken by scanning electron microscopy

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.

AI-Fueled Software Reveals Accurate Protein Structure Prediction

September 7, 2021

Researchers used artificial intelligence to generate hundreds of new protein structures, including this 3D view of human interleukin-12 bound to its receptor. (Photo credit: Ian Haydon)

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.

Chasing Their Tails But Getting Somewhere: Reimagining the Shape of Noise Leads to Improved Molecular Models

October 20, 2020

Univ. of Tennessee undergrad Elliot Perryman (on right) worked with Biosciences staff scientist Peter Zwart during his fall 2019 BLUR internship. (Credit: Thor Swift/Berkeley Lab)

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.

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