Dereplication of microbial metabolites through database search of mass spectra

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approache...

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Bibliographic Details
Published in:Nature communications 2018-10, Vol.9 (1), p.4035-12, Article 4035
Main Authors: Mohimani, Hosein, Gurevich, Alexey, Shlemov, Alexander, Mikheenko, Alla, Korobeynikov, Anton, Cao, Liu, Shcherbin, Egor, Nothias, Louis-Felix, Dorrestein, Pieter C., Pevzner, Pavel A.
Format: Article
Language:English
Subjects:
119/118
631/114/2164
631/92/349
Actinomyces - chemistry
Algorithms
Alkaloids
Benzenoids
Biological Products - analysis
Cyanobacteria - chemistry
Drug Discovery - methods
Flavonoids
Genomes
Genomics
Humanities and Social Sciences
Macrolides - analysis
Mass spectra
Mass Spectrometry
Metabolites
Microorganisms
multidisciplinary
Natural products
Polyketides
Science
Science (multidisciplinary)
Social organization
Software
Terpenes
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Summary:Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results. New natural products can be identified via mass spectrometry by excluding all known ones from the analysis, a process called dereplication. Here, the authors extend a previously published dereplication algorithm to different classes of secondary metabolites.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-018-06082-8