- Dr. Diwakar Shukla, Assistant Professor, Dept. of Chemical and Biomolecular Engineering
- Dr. Li-Qing Chen, Assistant Professor, Dept. of Plant Biology
Improving sugar allocation is important to increase crop yields for feeding the growing population. Genetic engineering of sugar partitioning offers a promising yet challenging strategy to achieve this goal. Many transporters from different families are involved in sugar partitioning at different tissues or cells. SWEETs transporters play critical roles in many physiological processes, including phloem loading and seed filling. Thus, engineering SWEETs into ones with higher substrate specificity or higher transport capacity, combining with CRISPR single-base editing strategy, offer new opportunities for crop improvement.
In this proposal, we aim to develop a platform that could enable rapid investigation of sequence-function relationships for plant membrane transporters. In particular, we employ a transfer learning approach to generate mutational scans from limited experimental data, transfer the knowledge from molecular simulations using Markov random field models to generate complete mutational scans of proteins. We generate experimental mutational scans for the AtSWEET family of proteins using functional assays including genetically encoded FRET measurements of transport activity. Preliminary results show that the transfer learning approach is able to predict the complete mutational scans and transfer knowledge between related proteins. Our results show that mutations guided by conformational dynamics observed in molecular dynamics simulations can be used to tune transport activity.
The proposed platform integrates the data science approaches and cutting-edge experimental techniques to not only provide higher-quality information but also the information that is inaccessible to current experimental approaches. This approach is generalizable to other families of related proteins to provide key information for understanding and tuning the transport activity differences between related proteins. The seed funding from the Center for Digital Agriculture would provide resources to demonstrate the applicability of the proposed methodology for a protein family critical for various physiological traits in plants.