Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences

Michael A. Skinnider, Chad W. Johnston, Mathusan Gunabalasingam, Nishanth J. Merwin, Agata M. Kieliszek, Robyn J. MacLellan, Haoxin Li, Michael R.M. Ranieri, Andrew L.H. Webster, My P.T. Cao, Annabelle Pfeifle, Norman Spencer, Q. Huy To, Dan Peter Wallace, Chris A. Dejong, Nathan A. Magarvey

Research output: Contribution to journalArticlepeer-review

152 Scopus citations


Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at

Original languageEnglish (US)
Article number6058
JournalNature communications
Issue number1
StatePublished - Dec 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy


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