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...

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Detalles Bibliográficos
Autores principales: Li, Mingchen, Kang, Liqi, Xiong, Yi, Wang, Yu Guang, Fan, Guisheng, Tan, Pan, Hong, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Methodology
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

Internet

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

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