Navigating high-order protein fitness landscapes via deep learning on directed evolution trajectories.

Abstract excerpt

Accurately predicting the fitness effects of high-order mutations is a grand challenge in understanding and engineering proteins. Existing models, including pretrained protein language models, struggle to capture the multiresidue interactions that govern these effects. Here, we introduce DENet, a deep learning framework that harnesses the rich comutation information within directed evolution (DE) trajectories to...

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