Research Insights
High-throughput biochemical, nuclear magnetic resonance, and computational predictive biology to decode glycosyltransferases of unknown function in Sorghum bicolor
Plants supply essential nutrients, fibers, and pharmaceuticals vital for human survival, and raw materials for numerous industries, including bioenergy, which supports jobs and drives economic development. Glycosyltransferases (GTs) catalyze the formation of glycosidic linkages to produce complex carbohydrates, which are highly abundant in all plants. This project will use high-throughput (HTP) biochemical, biophysical, and computational biology approaches to study carbohydrate metabolic processes in Sorghum bicolor, a platform energy crop. The integrated biochemical and biophysical data will enable the development of data-driven artificial intelligence and machine learning (AI/ML) computational tools to expand and accelerate functional prediction of plant gene products, efficiently linking genome sequence with gene function. The proposed research using S. bicolor as a model will provide functional information that can be extrapolated to decipher beneficial multigene traits across diverse plant species. The resulting foundational knowledge will drive innovation in the emerging bioeconomy, support US economic resilience, and advance initiatives focused on increased energy security.
Funder: U.S. Department of Energy
Amount: $1,915,000
PI: Breeanna Urbanowicz, Franklin College of Arts and Sciences, Department of Biochemistry and Molecular Biology