| 题名 | A deep learning framework to predict binding preference of RNA constituents on protein surface |
| 作者 | |
| 通讯作者 | Zhu，Lizhe; Chen，Wei; Huang，Xuhui; Gao，Xin |
| 发表日期 | 2019-12-01
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| DOI | |
| 发表期刊 | |
| ISSN | 2041-1723
|
| EISSN | 2041-1723
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| 卷号 | 10期号:1 |
| 摘要 | 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 constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins. |
| 相关链接 | [Scopus记录] |
| 收录类别 | |
| 语种 | 英语
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| 重要成果 | NI期刊
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| 学校署名 | 通讯
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| 资助项目 | Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government[KQTD20180411143432337]
; Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government[JCYJ20170307105752508]
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| WOS研究方向 | Science & Technology - Other Topics
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| WOS类目 | Multidisciplinary Sciences
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| WOS记录号 | WOS:000493275600015
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| 出版者 | |
| Scopus记录号 | 2-s2.0-85074261461
|
| 来源库 | Scopus
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| 引用统计 |
被引频次[WOS]：70
|
| 成果类型 | 期刊论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/43761 |
| 专题 | 生命科学学院_生物系 生命科学学院 |
| 作者单位 | 1.Computational Bioscience Research CenterComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology (KAUST),Thuwal,23955-6900,Saudi Arabia 2.Department of ChemistryThe Hong Kong University of Science and Technology,Hong Kong 3.Warshel Institute for Computational BiologySchool of Life and Health Sciencesthe Chinese University of Hong Kong (Shenzhen)Shenzhen,Guangdong,518172,China 4.Department of Biochemistry and Institute for Protein DesignUniversity of Washington,Seattle,United States 5.Laboratoire d’ InformatiqueDepartment of Computer ScienceÉcole Polytechnique,Palaiseau,France 6.Departments of MedicineGenetics and BioengineeringStanford University,Stanford,United States 7.Department of BiologySouthern University of Science and Technology,Shenzhen,518055,China 8.Division of Biomedical EngineeringThe Hong Kong University of Science and Technology,Hong Kong 9.State Key Laboratory of Molecular NeuroscienceThe Hong Kong University of Science and Technology,Hong Kong 10.Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & ReconstructionThe Hong Kong University of Science and Technology,Hong Kong 11.Institute for Advanced StudyThe Hong Kong University of Science and Technology,Hong Kong 12.HKUST-Shenzhen Research InstituteHi-Tech Park,Nanshan,518057,China |
| 通讯作者单位 | 生物系; 生命科学学院 |
| 推荐引用方式 GB/T 7714 |
Lam，Jordy Homing,Li，Yu,Zhu，Lizhe,et al. A deep learning framework to predict binding preference of RNA constituents on protein surface[J]. Nature Communications,2019,10(1).
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| APA |
Lam，Jordy Homing.,Li，Yu.,Zhu，Lizhe.,Umarov，Ramzan.,Jiang，Hanlun.,...&Gao，Xin.(2019).A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature Communications,10(1).
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| MLA |
Lam，Jordy Homing,et al."A deep learning framework to predict binding preference of RNA constituents on protein surface".Nature Communications 10.1(2019).
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| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | 操作 | |
| 10.1038@s41467-019-1（2518KB） | -- | -- | 开放获取 | -- | 浏览 | |
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