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PLMSearch: Protein language model powers accurate and fast sequence search for remote homology

Author

Listed:
  • Wei Liu

    (Fudan University)

  • Ziye Wang

    (Fudan University)

  • Ronghui You

    (Fudan University)

  • Chenghan Xie

    (Fudan University)

  • Hong Wei

    (Nankai University)

  • Yi Xiong

    (Shanghai Jiao Tong University)

  • Jianyi Yang

    (Shandong University)

  • Shanfeng Zhu

    (Fudan University
    Shanghai Qi Zhi Institute
    Ministry of Education
    Fudan University)

Abstract

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .

Suggested Citation

  • Wei Liu & Ziye Wang & Ronghui You & Chenghan Xie & Hong Wei & Yi Xiong & Jianyi Yang & Shanfeng Zhu, 2024. "PLMSearch: Protein language model powers accurate and fast sequence search for remote homology," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46808-5
    DOI: 10.1038/s41467-024-46808-5
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    References listed on IDEAS

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    1. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
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