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

Jordy Homing Lam, Yu Li, Lizhe Zhu (), Ramzan Umarov, Hanlun Jiang, Amélie Héliou, Fu Kit Sheong, Tianyun Liu, Yongkang Long, Yunfei Li, Liang Fang, Russ B. Altman, Wei Chen (), Xuhui Huang () and Xin Gao ()
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Jordy Homing Lam: Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST)
Yu Li: Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST)
Lizhe Zhu: Department of Chemistry, The Hong Kong University of Science and Technology
Ramzan Umarov: Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST)
Hanlun Jiang: Department of Biochemistry and Institute for Protein Design, University of Washington
Amélie Héliou: Laboratoire d’ Informatique, Department of Computer Science, École Polytechnique
Fu Kit Sheong: Department of Chemistry, The Hong Kong University of Science and Technology
Tianyun Liu: Departments of Medicine, Genetics and Bioengineering, Stanford University
Yongkang Long: Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST)
Yunfei Li: Department of Biology, Southern University of Science and Technology
Liang Fang: Department of Biology, Southern University of Science and Technology
Russ B. Altman: Departments of Medicine, Genetics and Bioengineering, Stanford University
Wei Chen: Department of Biology, Southern University of Science and Technology
Xuhui Huang: Department of Chemistry, The Hong Kong University of Science and Technology
Xin Gao: Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST)

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12920-0

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