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Precise prediction of phase-separation key residues by machine learning

Author

Listed:
  • Jun Sun

    (Sichuan University
    Sichuan University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Jiale Qu

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Cai Zhao

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Xinyao Zhang

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Xinyu Liu

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Jia Wang

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University
    Guangzhou Medical University)

  • Chao Wei

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Xinyi Liu

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Mulan Wang

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Pengguihang Zeng

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Xiuxiao Tang

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Xiaoru Ling

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Li Qing

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Shaoshuai Jiang

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Jiahao Chen

    (Sun Yat-sen University
    Sun Yat-sen University
    Sun Yat-sen University)

  • Tara S. R. Chen

    (Sun Yat-Sen University)

  • Yalan Kuang

    (Sichuan University
    Sichuan University)

  • Jinhang Gao

    (Sichuan University
    Sichuan University)

  • Xiaoxi Zeng

    (Sichuan University
    Sichuan University)

  • Dongfeng Huang

    (Sun Yat-Sen University)

  • Yong Yuan

    (Sichuan University
    Sichuan University)

  • Lili Fan

    (School of Traditional Chinese Medicine, Jinan University)

  • Haopeng Yu

    (Sichuan University
    Sichuan University)

  • Junjun Ding

    (Sichuan University
    Sichuan University
    Sun Yat-sen University
    Sun Yat-sen University)

Abstract

Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.

Suggested Citation

  • Jun Sun & Jiale Qu & Cai Zhao & Xinyao Zhang & Xinyu Liu & Jia Wang & Chao Wei & Xinyi Liu & Mulan Wang & Pengguihang Zeng & Xiuxiao Tang & Xiaoru Ling & Li Qing & Shaoshuai Jiang & Jiahao Chen & Tara, 2024. "Precise prediction of phase-separation key residues by machine learning," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46901-9
    DOI: 10.1038/s41467-024-46901-9
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    References listed on IDEAS

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