Journal
ACS SYNTHETIC BIOLOGY
Volume 9, Issue 11, Pages 2927-2935Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.0c00345
Keywords
computational protein design; neural networks; machine learning; protein engineering
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Funding
- Welch Foundation [F-1654]
- ARO (MURI) [SP0036191-PROJ0009952]
- NSF [1541244]
- Direct For Biological Sciences
- Div Of Molecular and Cellular Bioscience [1541244] Funding Source: National Science Foundation
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Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function in vivo across three diverse proteins by at least 5-fold. Furthermore, this model provides a means to interrogate the chemical space within protein microenvironments and identify specific chemical interactions that contribute to the gain-of-function phenotypes resulting from individual mutations.
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