Data-driven protease engineering by DNA-recording and epistasis-aware machine learning
Abstract Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilities to design protein sequences with desi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60622-7 |
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