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Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model

Nature Computational Science volume 4, pages 29–42 (2024)Cite this article

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Abstract

Signal peptides (SPs) are essential to target and transfer transmembrane and secreted proteins to the correct positions. Many existing computational tools for predicting SPs disregard the extreme data imbalance problem and rely on additional group information of proteins. Here we introduce Unbiased Organism-agnostic Signal Peptide Network (USPNet), an SP classification and cleavage-site prediction deep learning method. Extensive experimental results show that USPNet substantially outperforms previous methods on classification performance by 10%. An SP-discovering pipeline with USPNet is designed to explore unprecedented SPs from metagenomic data. It reveals 347 SP candidates, with the lowest sequence identity between our candidates and the closest SP in the training dataset at only 13%. In addition, the template modeling scores between candidates and SPs in the training set are mostly above 0.8. The results showcase that USPNet has learnt the SP structure with raw amino acid sequences and the large protein language model, thereby enabling the discovery of unknown SPs.

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Fig. 1: USPNet workflow for predicting SP and cleavage site.
Fig. 2: USPNet shows robust performance across different SP types and organism groups.
Fig. 3: Embedding and ablation study performance analysis of USPNet compared with alternative models.
Fig. 4: Performance of USPNet on domain-shift data.
Fig. 5: The exploration of metagenomics data for SP discovery.

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Data availability

All the datasets we used, including training data, benchmark data, independent test data, proteome-wide study results and metagenomic study results are listed in Methods and are available at https://doi.org/10.17605/OSF.IO/NH3CF ref. 49. Source data are provided with this paper.

Code availability

The open-source codes of USPNet can be found at https://github.com/ml4bio/USPNet and the Code Ocean software capsule https://doi.org/10.24433/CO.8184163.v1 ref. 50.

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Acknowledgements

Special thanks to the people who suggested that we evaluate models on the 40% cut-off benchmark set. The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (project number CUHK 24204023, to Y.L.) and a grant from Innovation and Technology Commission of the Hong Kong Special Administrative Region, China (project number GHP/065/21SZ, to Y.L.). The work was partially supported by the National Key R&D Program of China (NO.2022ZD0160101).

Author information

Author notes

  1. These authors contributed equally: Junbo Shen, Qinze Yu, Shenyang Chen.

Authors and Affiliations

  1. Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China

    Junbo Shen, Qinze Yu, Shenyang Chen, Qingxiong Tan, Jingchen Li & Yu Li

  2. Department of Computer Science and Engineering, Washington University, St. Louis, MO, US

    Junbo Shen

  3. The CUHK Shenzhen Research Institute, Shenzhen, China

    Shenyang Chen & Yu Li

  4. Georgia Institute of Technology, Atlanta, GA, US

    Shenyang Chen

  5. Shanghai Artificial Intelligence Laboratory, Shanghai, China

    Yu Li

  6. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yu Li

  7. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Yu Li

  8. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Yu Li

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Contributions

Y.L., J.S. and S.C. designed the computational method. J.S., Q.Y. and S.C. implemented the main algorithm. J.S., Q.Y., S.C., Q.T. and J.L. did the experiments. J.S. and Q.Y. performed the analysis. J.S., Q.Y. and S.C. wrote the paper. Y.L. supervised the project. All authors read and approved the paper.

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Correspondence to Yu Li.

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Nature Computational Science thanks Rita Casadio and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team.

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Shen, J., Yu, Q., Chen, S. et al. Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model. Nat Comput Sci 4, 29–42 (2024). https://doi.org/10.1038/s43588-023-00576-2

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