Filter News
Area of Research
News Topics
- (-) Clean Water (5)
- (-) Summit (7)
- 3-D Printing/Advanced Manufacturing (12)
- Advanced Reactors (4)
- Artificial Intelligence (14)
- Big Data (9)
- Bioenergy (19)
- Biology (28)
- Biomedical (6)
- Biotechnology (3)
- Buildings (16)
- Chemical Sciences (15)
- Climate Change (26)
- Composites (3)
- Computer Science (20)
- Coronavirus (9)
- Critical Materials (4)
- Cybersecurity (7)
- Decarbonization (21)
- Element Discovery (1)
- Energy Storage (25)
- Environment (36)
- Exascale Computing (8)
- Fossil Energy (1)
- Frontier (10)
- Fusion (7)
- Grid (13)
- High-Performance Computing (16)
- Hydropower (8)
- Irradiation (1)
- Isotopes (4)
- ITER (2)
- Machine Learning (10)
- Materials (37)
- Materials Science (16)
- Mercury (1)
- Microscopy (13)
- Nanotechnology (9)
- National Security (17)
- Net Zero (2)
- Neutron Science (12)
- Nuclear Energy (10)
- Partnerships (8)
- Physics (10)
- Polymers (5)
- Quantum Computing (7)
- Quantum Science (9)
- Security (4)
- Simulation (6)
- Space Exploration (4)
- Sustainable Energy (25)
- Transformational Challenge Reactor (2)
- Transportation (10)
Media Contacts
![An ORNL-led team studied the SARS-CoV-2 spike protein in the trimer state, shown here, to pinpoint structural transitions that could be disrupted to destabilize the protein and negate its harmful effects. Credit: Debsindhu Bhowmik/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/sars_cov_2_bk.png?h=05c2797f&itok=jQ2D9aTr)
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
![This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/thermosipho_collabfold2_0.jpg?h=3432ff3c&itok=4xhLbjKZ)
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.