Project Notice |
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| PNR | 41232 |
| Project Name | DEEP LEARNING PREDICTION OF PROTEIN COMPLEX STRUCTURES |
| Project Detail | Project Innovation + Advantages: The University of Missouri will develop deep learning methods to predict inter-protein amino acid interactions and build three-dimensional structures of protein complexes, which are useful for designing and engineering protein molecules important for renewable bioenergy production. Proteins in cells interact and form complexes to carry out various biological functions such as catalyzing biochemical reactions. The team will use the deep learning methods it develops to construct green algae protein complexes that play important roles in biomass and biodiesel production. The technology and predicted structures of protein complexes will become valuable tools and resources for advancing U.S. bioenergy production and research. |
| Funded By | Self-Funded |
| Sector | Entertainment |
| Country | United States , Northern America |
| Project Value | USD 447,458 |
Contact Information |
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| Company Name | University of Missouri |
| Address | ARPA-E Program Director: Dr. David Tew Press and General Inquiries Email: ARPA-E-Comms@hq.doe.gov (link sends e-mail) Project Contact: Dr. Jianlin Cheng Project Contact Email: chengji@missouri.edu |
| Web Site | https://arpa-e.energy.gov/?q=slick-sheet-project/deep-learning-prediction-protein-complex-structures |
