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Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences

Nature Biomedical Engineering volume 7, pages 797–810 (2023)Cite this article

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Abstract

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6–9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

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Fig. 1: Overview of the AMP screening approach.
Fig. 2: Model selection for the pipeline.
Fig. 3: Wet-lab validation of the antimicrobial activity of the predicted peptides against S. aureus.
Fig. 4: Screening in the expended peptide libraries.
Fig. 5: Biological properties of CRRI hexapeptides in vitro.
Fig. 6: Therapeutic efficacy in treating acute pneumonia in vivo.
Fig. 7: Therapeutic efficacy in treating chronic pneumonia in vivo.

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Data availability

The main data supporting the findings of this study are available within the Article and its Supplementary Information. Negative AMPs were collected from the UniProt database (http://www.uniprot.org). Positive AMPs were collected from an external dataset dubbed Grampa and from an internal dataset consisting of peptides that were internally synthesized and experimentally validated. Source data for the figures are provided with this paper.

Code availability

Codes for the machine-learning models and for the generation of peptide features are provided in Supplementary Information.

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Acknowledgements

We thank R. Liu from East China University of Science and Technology for assistance in testing antimicrobial activity against clinically isolated strains. This work was supported by the National Natural Science Foundation of China (51933009, to J.J.), the National Key Research and Development Program of China (2020YFE0204400, to P.Z.), the Zhejiang Provincial Ten Thousand Talents Program (2018R52001, to J.J.), the Fundamental Research Funds for the Central Universities (226-2022-00146, to P.Z.), the International Research Center for X Polymers (to J.H.) and the startup package from Zhejiang University (to P.Z.).

Author information

Author notes

  1. These authors contributed equally: Junjie Huang, Yanchao Xu.

Authors and Affiliations

  1. MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China

    Junjie Huang, Yunfan Xue, Yue Huang, Xu Li, Xiaohui Chen, Peng Zhang & Jian Ji

  2. International Research Center for X Polymers, International Campus, Zhejiang University, Haining, China

    Junjie Huang, Yunfan Xue, Peng Zhang & Jian Ji

  3. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Yanchao Xu, Dongxiang Zhang & Junbo Zhao

  4. R&D Department of AtaGenix Laboratories Co., Ltd. (Wuhan), Wuhan, China

    Yao Xu

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Contributions

P.Z., J.Z. and J.J. conceived and supervised the project. J.H., Yanchao Xu and Y. Xue managed the project. Y. Xue encoded the peptide sequences. Yanchao Xu and J.Z. designed and implemented the algorithm. J.H., P.Z. and J.J. designed the wet-laboratory experiments. J.H. conducted the wet-laboratory experiments with the help of Y.H., X.C. and X.L. Yao Xu and D.Z. provided conceptual advice and technical support. J.H., Yanchao Xu, P.Z. and J.Z. prepared the manuscript. All of the authors discussed the results and assisted in the preparation of the manuscript.

Corresponding authors

Correspondence to Peng Zhang, Junbo Zhao or Jian Ji.

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Nature Biomedical Engineering thanks Cesar de la Fuente, Kim Lewis, Xiangrong Liu and Fangping Wan for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary File

Codes for the machine-learning models and for the generation of peptide features.

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Huang, J., Xu, Y., Xue, Y. et al. Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nat. Biomed. Eng 7, 797–810 (2023). https://doi.org/10.1038/s41551-022-00991-2

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