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Mining for antimicrobial peptides in sequence space

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A machine-learning pipeline identifies potent antimicrobial peptides by gradually narrowing down the search space of polypeptide chain sequences.

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Fig. 1: A machine-learning pipeline for the discovery of AMPs.

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Authors and Affiliations

  1. Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Fangping Wan & Cesar de la Fuente-Nunez

  2. Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Fangping Wan & Cesar de la Fuente-Nunez

  3. Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA

    Fangping Wan & Cesar de la Fuente-Nunez

Authors
  1. Fangping Wan
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  2. Cesar de la Fuente-Nunez
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Correspondence to Cesar de la Fuente-Nunez.

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The authors declare no competing interests.

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Wan, F., de la Fuente-Nunez, C. Mining for antimicrobial peptides in sequence space. Nat. Biomed. Eng 7, 707–708 (2023). https://doi.org/10.1038/s41551-023-01027-z

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