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A mass spectrometry-guided genome mining approach for natural product peptidogenomics.


ABSTRACT: Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based structures following biosynthetic logic. In this study, we show that NPP enabled the rapid characterization of over ten chemically diverse ribosomal and nonribosomal peptide natural products of previously unidentified composition from Streptomycete bacteria as a proof of concept to begin automating the genome-mining process. We show the identification of lantipeptides, lasso peptides, linardins, formylated peptides and lipopeptides, many of which are from well-characterized model Streptomycetes, highlighting the power of NPP in the discovery of new peptide natural products from even intensely studied organisms.

SUBMITTER: Kersten RD 

PROVIDER: S-EPMC3258187 | biostudies-other | 2011 Oct

REPOSITORIES: biostudies-other

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A mass spectrometry-guided genome mining approach for natural product peptidogenomics.

Kersten Roland D RD   Yang Yu-Liang YL   Xu Yuquan Y   Cimermancic Peter P   Nam Sang-Jip SJ   Fenical William W   Fischbach Michael A MA   Moore Bradley S BS   Dorrestein Pieter C PC  

Nature chemical biology 20111009 11


Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based st  ...[more]

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