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SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
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 fitness for...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898993/ https://www.ncbi.nlm.nih.gov/pubmed/36737798 http://dx.doi.org/10.1186/s13321-023-00688-x |