A deep learning framework to predict binding preference of RNA constituents on protein surface
Type
ArticleAuthors
Lam, Jordy HomingLi, Yu

Zhu, Lizhe

Umarov, Ramzan
Jiang, Hanlun
Héliou, Amélie
Sheong, Fu Kit
Liu, Tianyun
Long, Yongkang

Li, Yunfei
Fang, Liang
Altman, Russ B.
Chen, Wei
Huang, Xuhui

Gao, Xin

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
Date
2019-10-30Online Publication Date
2019-10-30Print Publication Date
2019-12Permanent link to this record
http://hdl.handle.net/10754/659549
Metadata
Show full item recordAbstract
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/s41467-019-12920-0Sponsors
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 LLCJournal
Nature CommunicationsAdditional Links
http://www.nature.com/articles/s41467-019-12920-0Relations
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