Research
The Ennist Lab uses AI-guided protein design to engineer new proteins for enhanced photosynthesis.
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Photosynthesis sustains the biosphere, but it operates with low overall efficiency. By designing novel proteins for high-efficiency photosynthesis, we hope to engineer plants and algae to increase production of food, renewable solar fuels, and sustainable ammonia for fertilizer. Using machine learning and experimental biophysics, we design and test proteins that assemble networks of pigment molecules, electron-transport chains, and metal clusters for enzymatic catalysis. Our long-term goal is to use our de novo-designed proteins to increase food and energy security by improving production of renewable solar fuels, sustainable ammonia for fertilizer, food, and other useful products.
Natural oxygenic photosynthesis uses a “Z-scheme” in which photosystems I and II (PSI and PSII) work in series to convert visible light into chemical energy. Photosystems are protein assemblies composed of light-harvesting complexes and reaction centers (RCs) that separate charge. Our group is working to design and test two alternative types of photosystems. First, we aim to incorporate near-infrared (NIR) light into oxygenic photosynthesis using bacteriochlorin molecules that can absorb wavelengths up to 1000 nm. This would increase the solar photon flux available for oxygenic photosynthesis by over 90%. Second, we seek to combine the oxidizing activity of PSII and the reducing activity of PSI into a single photosystem that produces the same amount of NADPH with half as many visible photons as the natural Z-scheme. These two approaches represent complementary strategies for increasing the overall efficiency of photosynthesis, potentially leading to improved production of renewable solar fuels, sustainable ammonia for fertilizer, food, and other useful products.
SP proteins and image, Chl rings, FeS, M4O4, flavins,