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

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    Type
    Article
    Authors
    Lam, Jordy Homing
    Li, Yu cc
    Zhu, Lizhe cc
    Umarov, Ramzan
    Jiang, Hanlun
    Héliou, Amélie
    Sheong, Fu Kit
    Liu, Tianyun
    Long, Yongkang cc
    Li, Yunfei
    Fang, Liang
    Altman, Russ B.
    Chen, Wei
    Huang, Xuhui cc
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    FCC/1/1976-18-01
    FCC/1/1976-23-01
    FCC/1/1976-25-01
    FCC/1/1976-26-01
    URF/1/3007-01
    Date
    2019-10-30
    Online Publication Date
    2019-10-30
    Print Publication Date
    2019-12
    Permanent link to this record
    http://hdl.handle.net/10754/659549
    
    Metadata
    Show full item record
    Abstract
    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.
    Citation
    Lam, J. H., Li, Y., Zhu, L., Umarov, R., Jiang, H., Héliou, A., … Gao, X. (2019). A deep learning framework to predict binding preference of RNA constituents on protein surface. Nature Communications, 10(1). doi:10.1038/s41467-019-12920-0
    Sponsors
    We are grateful to Wei Wang for helpful discussions. Figure 1 was created by Heno Hwang, scientific illustrator at King Abdullah University of Science and Technology (KAUST). This work was supported by grants from KAUST to X.G. (BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, and FCS/1/4102-02-01) and funding from the KAUST to X.G. and X.H. (URF/1/3007-01). The Hong Kong Research Grant Council (HKUST C6009-15G, AoE/M-09/12, and AoE/P-705/16) and Innovation and Technology Commission (ITCPD/17-9 and ITC-CNERC14SC01) to X.H.; L.F., Y.F.L., and W.C. were supported by Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government (No. KQTD20180411143432337 and JCYJ20170307105752508). Part of bioinformatics analysis was supported by the Center for Computational Science and Engineering of Southern University of Science and Technology.
    Publisher
    Springer Science and Business Media LLC
    Journal
    Nature Communications
    DOI
    10.1038/s41467-019-12920-0
    Additional Links
    http://www.nature.com/articles/s41467-019-12920-0
    Relations
    Is Supplemented By:
    • [Software]
      Title: NucleicNet/NucleicNet:. Publication Date: 2019-05-28. github: NucleicNet/NucleicNet Handle: 10754/666990
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41467-019-12920-0
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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