A deep learning framework to predict binding preference of RNA constituents on protein surface
Interactions between proteins and RNA are an important mechanism for post-transcriptional regulation, but predicting these interactions is difficult. Through a deep learning approach, here the authors predict RNA-binding sites and binding preference based on the local physicochemical properties of t...
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Nature Portfolio
2019
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| Acceso en línea: | https://doaj.org/article/361a2a5e8af64738999759aa8027bf6e |
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oai:doaj.org-article:361a2a5e8af64738999759aa8027bf6e2021-12-02T14:38:46ZA deep learning framework to predict binding preference of RNA constituents on protein surface10.1038/s41467-019-12920-02041-1723https://doaj.org/article/361a2a5e8af64738999759aa8027bf6e2019-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12920-0https://doaj.org/toc/2041-1723Interactions between proteins and RNA are an important mechanism for post-transcriptional regulation, but predicting these interactions is difficult. Through a deep learning approach, here the authors predict RNA-binding sites and binding preference based on the local physicochemical properties of the protein surface.Jordy Homing LamYu LiLizhe ZhuRamzan UmarovHanlun JiangAmélie HéliouFu Kit SheongTianyun LiuYongkang LongYunfei LiLiang FangRuss B. AltmanWei ChenXuhui HuangXin GaoNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-13 (2019) |
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DOAJ |
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DOAJ |
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EN |
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Science Q |
| spellingShingle |
Science Q Jordy Homing Lam Yu Li Lizhe Zhu Ramzan Umarov Hanlun Jiang Amélie Héliou Fu Kit Sheong Tianyun Liu Yongkang Long Yunfei Li Liang Fang Russ B. Altman Wei Chen Xuhui Huang Xin Gao A deep learning framework to predict binding preference of RNA constituents on protein surface |
| description |
Interactions between proteins and RNA are an important mechanism for post-transcriptional regulation, but predicting these interactions is difficult. Through a deep learning approach, here the authors predict RNA-binding sites and binding preference based on the local physicochemical properties of the protein surface. |
| format |
article |
| author |
Jordy Homing Lam Yu Li Lizhe Zhu Ramzan Umarov Hanlun Jiang Amélie Héliou Fu Kit Sheong Tianyun Liu Yongkang Long Yunfei Li Liang Fang Russ B. Altman Wei Chen Xuhui Huang Xin Gao |
| author_facet |
Jordy Homing Lam Yu Li Lizhe Zhu Ramzan Umarov Hanlun Jiang Amélie Héliou Fu Kit Sheong Tianyun Liu Yongkang Long Yunfei Li Liang Fang Russ B. Altman Wei Chen Xuhui Huang Xin Gao |
| author_sort |
Jordy Homing Lam |
| title |
A deep learning framework to predict binding preference of RNA constituents on protein surface |
| title_short |
A deep learning framework to predict binding preference of RNA constituents on protein surface |
| title_full |
A deep learning framework to predict binding preference of RNA constituents on protein surface |
| title_fullStr |
A deep learning framework to predict binding preference of RNA constituents on protein surface |
| title_full_unstemmed |
A deep learning framework to predict binding preference of RNA constituents on protein surface |
| title_sort |
deep learning framework to predict binding preference of rna constituents on protein surface |
| publisher |
Nature Portfolio |
| publishDate |
2019 |
| url |
https://doaj.org/article/361a2a5e8af64738999759aa8027bf6e |
| work_keys_str_mv |
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