Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.

Flexible, high-throughput proteomics approach identifies protein modifications related to pancreatic stress and insulin production.

Scientists map how transitions from day to night control gene regulatory networks in cyanobacteria, revealing key orchestrators of metabolic switching.

Scientists uncover how cyanobacteria's circadian cycle controls carbon and energy metabolism, increasing carbon dioxide-to-bioproducts yields.

PNNL is hosting a scientific conference focused directly on understanding and predicting the collection of an organism’s traits.

Circadian rhythms of blue-green microbes offer new window into predictive phenomics—how to predict and control the traits of an organism.

Nine national laboratories, including PNNL, bring together over 1,000 scientists for a day to use AI to accelerate scientific discovery.

This week at AAAS, PNNL scientists discuss phenomics and the factors that influence DNA instructions to bring about the world around us.

PNNL scientists use proteomics to unveil how high-density lipoproteins protect against cholesterol accumulation in the artery wall.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

Researchers will explore climate, pathogens and energy-efficient microelectronics using 3 million node hours on the nation’s supercomputers.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.

Scientists developed a process (or pipeline) that combined molecular probes—a specific chemical that binds to microbes carrying out a particular function—with a method that isolated these cells from their complex community.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.