SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Abstract Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fit...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-02-01
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| Series: | Journal of Cheminformatics |
| Online Access: | https://doi.org/10.1186/s13321-023-00688-x |