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题名	A deep learning framework to predict binding preference of RNA constituents on protein surface
作者	Lam，Jordy Homing 1,2; Li，Yu 1; Zhu，Lizhe 2,3; Umarov，Ramzan 1; Jiang，Hanlun 4; Héliou，Amélie 5; Sheong，Fu Kit 2; Liu，Tianyun 6; Long，Yongkang1,7 ; Li，Yunfei7 ; Fang，Liang7 ; Altman，Russ B.6; Chen，Wei7 ; Huang，Xuhui 2,8,9,10,11,12; Gao，Xin 1
通讯作者	Zhu，Lizhe; Chen，Wei; Huang，Xuhui; Gao，Xin
发表日期	2019-12-01
DOI	10.1038/s41467-019-12920-0
发表期刊	Nature Communications 影响因子和分区
ISSN	2041-1723
EISSN	2041-1723
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	SCI
语种	英语
重要成果	NI期刊 ; NI论文
学校署名	通讯
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]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000493275600015
出版者	NATURE PUBLISHING GROUP
Scopus记录号	2-s2.0-85074261461
来源库	Scopus
引用统计	被引频次[WOS]：63
成果类型	期刊论文
条目标识符	//www.snoollab.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).
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).
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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