Europe PMC

This website requires cookies, and the limited processing of your personal data in order to function. By using the site you are agreeing to this as outlined in our privacy notice and cookie policy.

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Author information

Affiliations

  • 1. Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil.
      Authors
    • Santos-Júnior CD 1
    (1 author)
  • 2. 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; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
      Authors
    • Torres MDT 2
    • de la Fuente-Nunez C 2
    (2 authors)
  • 3. Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China.
      Authors
    • Duan Y 3
    • Chong H 3
    • Zhu C 3
    • Houseman A 3
    • Somborski J 3
    • Vines A 3
    (6 authors)
  • 4. Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain.
      Authors
    • Rodríguez Del Río Á 4
    • Huerta-Cepas J 4
    (2 authors)
  • 5. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland.
      Authors
    • Schmidt TSB 5
    (1 author)
    • Show all (9)

ORCIDs linked to this article

Cell, 05 Jun 2024, 187(14):3761-3778.e16
https://doi.org/10.1016/j.cell.2024.05.013 PMID: 38843834 

This is an update of "" bioRxiv. 2023 Sep 11:2023.08.31.555663. doi: 10.1101/2023.08.31.555663. A comment on this article appears in "Machine learning identifies AMPs." Nat Rev Drug Discov. 2024 Aug;23(8):581. doi: 10.1038/d41573-024-00111-6.

Abstract 

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Full text links 

Read article at publisher's site: https://doi.org/10.1016/j.cell.2024.05.013

Citations & impact 

Impact metrics

9
Citations
Jump to Citations

Citations of article over time

Alternative metrics

Altmetric item for https://www.altmetric.com/details/164228163
Altmetric
Discover the attention surrounding your research
https://www.altmetric.com/details/164228163

Article citations

Go to all (9) article citations

Similar Articles 

To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.

Funding 

Funders who supported this work.

NIGMS NIH HHS (1)