[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126915-en":3,"doc-seo-126915-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126915,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Discovery of antimicrobial peptides in the global microbiome with machine learning - Graphical abstract & Highlights","Machine-learning-based modeling is used to discover antimicrobial peptides (AMPs) from the global microbiome and to build AMPSphere, a comprehensive open-access catalog. The resource is generated from 63,410 metagenomes and 87,920 prokaryotic genomes, yielding 863,498 nonredundant peptides with limited matches to existing databases. Predictions are experimentally validated by synthesizing and testing 100 AMPs against drug-resistant pathogens and gut commensals, with 79 active peptides and 63 targeting pathogens. Active AMPs disrupt bacterial membranes in vitro and in vivo.","Resource  \nDiscovery of antimicrobial peptides in the global microbiome with machine learning  \nGraphical abstract  \nHighlights  \nAuthors  \nC´elio Dias Santos-J´unior,  \nMarcelo D.T. Torres, Yiqian Duan, ..., Jaime Huerta-Cepas,  \nCesar de la Fuente-Nunez, Luis Pedro Coelho  \nCorrespondence  \n[cfuente@upenn.edu](cfuente@upenn.edu) (C.d.l.F.-N.),  \n[luispedro@big-data-biology.org](luispedro@big-data-biology.org) (L.P.C.)  \nIn brief  \nA machine-learning-based approach predicts nearly one million new antibiotics from the global microbiome, with 79 out of 100 tested peptides being active in vitro and several showing efficacy comparable to a clinical antibiotic in a mouse preclinical model of infection.  \nd Machine learning predicts nearly 1 million new antibiotics in the global microbiome  \nd Out of 100 tested peptides, 79 were active in vitro; 63 of these targeted pathogens  \nd Some peptides may originate from longer sequences through genomic fragmentation  \nd The AMPSphere is an open-access resource to accelerate antibiotic discovery  \nSantos-J´unior et al., 2024, Cell 187, 3761–3778  \nJuly 11, 2024 ª 2024 The Authors. Published by Elsevier Inc.  \n[https://doi.org/10.1016/j.cell.2024.05.013](https://doi.org/10.1016/j.cell.2024.05.013)  \nll  \nll  \nOPEN ACCESS  \nResource  \nDiscovery of antimicrobial peptides  \nin the global microbiome with machine learning  \nC´elio Dias Santos-J´unior,1,2,16 Marcelo D.T. Torres,3,4,5,6,16 Yiqian Duan,1 ´Alvaro Rodr´ıguez del R´ıo,7 Thomas S.B. Schmidt,8,9 Hui Chong,1 Anthony Fullam,8 Michael Kuhn,8 Chengkai Zhu,1 Amy Houseman,1 Jelena Somborski,1 Anna Vines,1 Xing-Ming Zhao,1,12,13,14 Peer Bork,8,10,11 Jaime Huerta-Cepas,7 Cesar de la Fuente-Nunez,3,4,5,6,* and Luis Pedro Coelho1,15,17,*  \n1Institute of Science and Technology for Brain-Inspired Intelligence-ISTBI, Fudan University, Shanghai 200433, China  \n2Laboratory of Microbial Processes & Biodiversity-LMPB, Department of Hydrobiology, Universidade Federal de S˜ao Carlos – UFSCar, S˜ao Carlos, S˜ao Paulo 13565-905, Brazil  \n3Machine 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  \n4Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA  \n5Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA  \n6Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA  \n7Centro de Biotecnologay Gen´omica de Plantas, Universidad Polit´ecnica de Madrid (UPM) -Instituto Nacional de Investigaci´ony Tecnologa Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarc´on, 28223 Madrid, Spain  \n8Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany  \n9APC Microbiome & School of Medicine, University College Cork, Cork, Ireland  \n10Max Delbr¨uck Centre for Molecular Medicine, Berlin, Germany  \n11Department of Bioinformatics, Biocenter, University of W¨urzburg, W¨urzburg, Germany  \n12Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China  \n13State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China  \n14MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China  \n15Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia  \n16These authors contributed equally  \n17Lead contact  \n*Correspondence: [cfuente@upenn.edu](cfuente@upenn.edu) (C.d.l.F.-N.), [luispedro@big-data-biology.org](luispedro@big-data-biology.org) (L.P.C.) 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