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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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.
Codes for the machine-learning models and for the generation of peptide features are provided in Supplementary Information.
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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.).
The authors declare no competing interests.
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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 (2023). https://doi.org/10.1038/s41551-022-00991-2