Discovery of antimicrobial peptides in the global microbiome with machine learning
Universidade Federal de São Carlos · Fudan University · +18 more institutions
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…
Citation impact
- FWCI
- 98.72
- Percentile
- 100%
- References
- 165
Authors
17- CDCélio Dias Santos Júnior
Universidade Federal de São Carlos, Fudan University, Shanghai Institute for Science of Science, Shanghai Center for Brain Science and Brain-Inspired Technology
- MDMarcelo D. T. Torres
Translational Therapeutics (United States), University of Pennsylvania
- YDYiqian Duan
Fudan University, Shanghai Institute for Science of Science, Shanghai Center for Brain Science and Brain-Inspired Technology
- ÁRÁlvaro Rodríguez del Río
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Centre for Plant Biotechnology and Genomics, Universidad Politécnica de Madrid
- TSThomas Schmidt
University College Cork, European Molecular Biology Laboratory, APC Microbiome Institute, European Molecular Biology Laboratory
Topics & keywords
- Biology
- Antimicrobial peptides
- Computational biology
- Microbiome
- Metagenomics
- Antibiotics
- Genome
- Antibiotic resistance
Funding
- BABrain and Behavior Research Foundation
- PAProcter and Gamble
- UOUniversity of Pennsylvania
- UTUnited Therapeutics Corporation
- EMEuropean Molecular Biology Laboratory
- NKNational Key Laboratory of Human Factors Engineering
- AIAmerican Institute of Chemical Engineers
- AFACE Foundation
- QUQueensland University of Technology
- NNNational Natural Science Foundation of ChinaAwards: 61932008, T2225015
- FEFederación Española de Enfermedades Raras
- SAScience and Technology Commission of Shanghai MunicipalityAwards: 22JC1410900, 2018SHZDZX01, 23JS1410100
- UZUniversität Zürich
- FBFundación Bancaria Caixa d'Estalvis i Pensions de BarcelonaAward: LCF/BQ/DI18/11660009
- NINational Institutes of Health
- DTDefense Threat Reduction Agency
- PSPerelman School of Medicine, University of Pennsylvania
- H2Horizon 2020 Framework Programme
- ARAustralian Research CouncilAward: FT230100724
- NKNational Key Research and Development Program of ChinaAwards: 2023YFF1204800, 2020YFA0712403
- HMH2020 Marie Skłodowska-Curie ActionsAward: 713673