Abstract

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.

Details

Title
Dereplication of microbial metabolites through database search of mass spectra
Author
Mohimani, Hosein 1 ; Gurevich, Alexey 2 ; Shlemov, Alexander 2   VIAFID ORCID Logo  ; Mikheenko, Alla 2 ; Korobeynikov, Anton 3   VIAFID ORCID Logo  ; Cao, Liu 4 ; Shcherbin, Egor 5 ; Louis-Felix Nothias 6 ; Dorrestein, Pieter C 7 ; Pevzner, Pavel A 8   VIAFID ORCID Logo 

1  Computational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA 
2  Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia 
3  Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Department of Statistical Modelling, St. Petersburg State University, St. Petersburg, Russia 
4  Computational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA, USA 
5  National Research University Higher School of Economics, St. Petersburg, Russia 
6  Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA 
7  Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Department of Pharmacology and Pediatrics, University of California, San Diego, La Jolla, CA, USA 
8  Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA; Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia 
Pages
1-12
Publication year
2018
Publication date
Oct 2018
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41467-018-06082-8
ProQuest document ID
2115732375
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.