A deep learning framework to predict binding preference of RNA constituents on protein surface

Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone consti...

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Detalles Bibliográficos
Autores principales: Lam, Jordy Homing, Li, Yu, Zhu, Lizhe, Umarov, Ramzan, Jiang, Hanlun, Héliou, Amélie, Sheong, Fu Kit, Liu, Tianyun, Long, Yongkang, Li, Yunfei, Fang, Liang, Altman, Russ B., Chen, Wei, Huang, Xuhui, Gao, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821705/
https://www.ncbi.nlm.nih.gov/pubmed/31666519
http://dx.doi.org/10.1038/s41467-019-12920-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821705/
https://www.ncbi.nlm.nih.gov/pubmed/31666519
http://dx.doi.org/10.1038/s41467-019-12920-0

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