Deep Learning Prediction of Protein Complex Structures

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Columbia, Missouri
Project Term:
03/10/2020 - 09/09/2022

Technology Description:

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:


Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.


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.


Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.


ARPA-E Program Director:
Dr. David Tew
Project Contact:
Dr. Jianlin Cheng
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Project Contact Email:

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