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

Journal: Cell
Published Date: Jun 5, 2024

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.

Authors

  • Célio Dias Santos-Júnior
    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.
  • Marcelo D T Torres
    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.
  • Yiqian Duan
    Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China.
  • Álvaro Rodríguez Del Río
    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.
  • Thomas S B Schmidt
    Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland.
  • Hui Chong
    MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Anthony Fullam
    Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
  • Michael Kuhn
    School of Earth and Planetary Science, Spatial Science Discipline, Curtin University, Perth 6102, Australia.
  • Chengkai Zhu
    Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China.
  • Amy Houseman
    Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China.
  • Jelena Somborski
    Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China.
  • Anna Vines
    Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China.
  • Xing-Ming Zhao
    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
  • Peer Bork
    Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.
  • Jaime Huerta-Cepas
    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.
  • Cesar de la Fuente-Nunez
    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.
  • Luis Pedro Coelho
    Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia. Electronic address: luispedro@big-data-biology.org.

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