A deep learning framework to predict binding preference of RNA constituents on protein surface
AuthorsLam, Jordy Homing
Sheong, Fu Kit
Altman, Russ B.
KAUST 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.
Computer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2019-10-30
Print Publication Date2019-12
Permanent link to this recordhttp://hdl.handle.net/10754/659549
MetadataShow full item record
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.
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
SponsorsWe 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.
PublisherSpringer Science and Business Media LLC
RelationsIs Supplemented By: