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Deep Learning Prediction of Protein Complex Structures

University of Missouri
Program: 
ARPA-E Award: 
$447,458
Location: 
Columbia, MO
Project Term: 
03/10/2020 to 03/09/2022
Project Status: 
ACTIVE
Technical Categories: 
Critical Need: 
The DIFFERENTIATE program seeks to leverage the emerging artificial intelligence (AI) revolution to help resolve the energy and environmental challenges of our time. The program aims to speed energy innovation by incorporating machine learning (ML) into the energy technology development process. A core part of AI, ML is the study of computer algorithms that improve automatically through experience. This approach is expected to facilitate a rapid transition to lower-carbon-footprint energy sources and systems. To organize the proposed efforts, the program uses a simplified engineering design process framework to conceptualize several ML tools that could help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.
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.
Potential Impact: 
DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:
Security: 
Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.
Environment: 
Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.
Economy: 
Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.
Contacts
ARPA-E Program Director: 
Dr. David Tew
Project Contact: 
Dr. Jianlin Cheng
Release Date: 
4/5/2019