Deep-learning structure elucidation from single-mutant deep mutational scanning

Zachary C. Drake, Elijah H. Day, Paul D. Toth and Steffen Lindert ()
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Zachary C. Drake: Los Angeles
Elijah H. Day: Los Angeles
Paul D. Toth: Ohio State University
Steffen Lindert: Los Angeles

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Deep learning has revolutionized the field of protein structure prediction. AlphaFold2, a deep neural network, vastly outperformed previous algorithms to provide near atomic-level accuracy when predicting protein structures. Despite its success, there still are limitations which prevent accurate predictions for numerous protein systems. Here we show that sparse residue burial restraints from deep mutational scanning (DMS) can refine AlphaFold2 to significantly enhance results. Burial information extracted from DMS is used to explicitly guide residue placement during structure generation. DMS-Fold was validated on both simulated and experimental single-mutant DMS, with DMS-Fold outperforming AlphaFold2 for 88% of protein targets and with 252 proteins having an improvement greater than 0.1 in TM-Score. DMS-Fold is free and publicly available: [ https://github.com/LindertLab/DMS-Fold ].

Date: 2025
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DOI: 10.1038/s41467-025-62261-4

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