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dc.contributor.authorLam, Jordy Homing
dc.contributor.authorLi, Yu
dc.contributor.authorZhu, Lizhe
dc.contributor.authorUmarov, Ramzan
dc.contributor.authorJiang, Hanlun
dc.contributor.authorHéliou, Amélie
dc.contributor.authorSheong, Fu Kit
dc.contributor.authorLiu, Tianyun
dc.contributor.authorLong, Yongkang
dc.contributor.authorLi, Yunfei
dc.contributor.authorFang, Liang
dc.contributor.authorAltman, Russ B.
dc.contributor.authorChen, Wei
dc.contributor.authorHuang, Xuhui
dc.contributor.authorGao, Xin
dc.date.accessioned2019-11-07T07:38:32Z
dc.date.available2019-11-07T07:38:32Z
dc.date.issued2019-10-30
dc.identifier.citationLam, 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
dc.identifier.doi10.1038/s41467-019-12920-0
dc.identifier.urihttp://hdl.handle.net/10754/659549
dc.description.abstractProtein-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.
dc.description.sponsorshipWe 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.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttp://www.nature.com/articles/s41467-019-12920-0
dc.rightsArchived with thanks to Nature Communications
dc.titleA deep learning framework to predict binding preference of RNA constituents on protein surface
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalNature Communications
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Chemistry, The Hong Kong University of Science and Technology, Hong Kong, China.
dc.contributor.institutionDepartment of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA.
dc.contributor.institutionLaboratoire d' Informatique, Department of Computer Science, École Polytechnique, Palaiseau, France.
dc.contributor.institutionDepartments of Medicine, Genetics and Bioengineering, Stanford University, Stanford, CA, USA.
dc.contributor.institutionDepartment of Biology, Southern University of Science and Technology, 518055, Shenzhen, Guangdong, China.
kaust.personLam, Jordy Homing
kaust.personLi, Yu
kaust.personUmarov, Ramzan
kaust.personLong, Yongkang
kaust.personGao, Xin
kaust.grant.numberFCC/1/1976-18-01
kaust.grant.numberFCC/1/1976-23-01
kaust.grant.numberFCC/1/1976-25-01
kaust.grant.numberFCC/1/1976-26-01
kaust.grant.numberURF/1/3007-01
refterms.dateFOA2019-11-07T07:39:16Z
kaust.acknowledged.supportUnitscientific illustrator
dc.date.published-online2019-10-30
dc.date.published-print2019-12


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