A mass spectrometry–guided genome mining approach for natural product peptidogenomics

Peptidic natural products are theoretically amenable to characterization by mass spectrometry, but proteomics programs are not trained to discover these compounds. A new strategy uses mass spectrometry and bioinformatics iteratively to rapidly identify both ribosomal and nonribosomal sequences, yielding multiple new compounds. 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.