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

Author

Listed:
  • Jordy Homing Lam

    (Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST)
    Department of Chemistry, The Hong Kong University of Science and Technology)

  • 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
    Warshel Institute for Computational Biology, School of Life and Health Sciences, the Chinese University of Hong Kong (Shenzhen), Shenzhen)

  • 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)
    Department of Biology, Southern University of Science and Technology)

  • 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
    The Hong Kong University of Science and Technology
    The Hong Kong University of Science and Technology
    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))

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.

Suggested Citation

  • 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 & X, 2019. "A deep learning framework to predict binding preference of RNA constituents on protein surface," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12920-0
    DOI: 10.1038/s41467-019-12920-0
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    Cited by:

    1. Shengbo Wu & Jie Feng & Chunjiang Liu & Hao Wu & Zekai Qiu & Jianjun Ge & Shuyang Sun & Xia Hong & Yukun Li & Xiaona Wang & Aidong Yang & Fei Guo & Jianjun Qiao, 2022. "Machine learning aided construction of the quorum sensing communication network for human gut microbiota," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Haoran Zhu & Yuning Yang & Yunhe Wang & Fuzhou Wang & Yujian Huang & Yi Chang & Ka-chun Wong & Xiangtao Li, 2023. "Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet," Nature Communications, Nature, vol. 14(1), pages 1-22, December.

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