Functional Characterization of Glycosyltransferases in Duckweed to Enable Predictive Biology
Authors:
Pradeep Kumar Prabhakar1, Digantkumar Gopaldas Chapla1, Charles Joseph Corulli1, Brayden Paul Smith1, Samantha Hennen2, Morgan J. Willis1, Tasleem Javaid1, Samantha J. Ziegler2, Vivek S. Bharadwaj2, Kelley W. Moremen1, Maria J. Pena1, Yannick J. Bomble2, and Breeanna R. Urbanowicz1*
Institutions:
1University of Georgia; and 2National Renewable Energy Laboratory
Abstract
Glycosyltransferases (GTs) catalyze the formation of glycosidic linkages to produce complex carbohydrates. This project involves the use of a multidisciplinary, high-throughput biochemical and computational biology approach to study carbohydrate metabolic processes in duckweed, a promising energy crop. The role of enzymatic microenvironments is being assessed through a combined proteomic and computational biology approach. This combined data will be used to populate a deep-learning framework to predict plant GT function.
Functional validation achieved through this research will be used to assign gene function and study plant processes at the systems level to efficiently link genome sequence with gene function in a feedstock agnostic manner.
Funding Information
This research is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research (BER) Program, Genomic Science program grant no. DE-SC0023223.