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Article

Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides

1
Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg 199004, Russia
2
Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, Sochi 354340, Russia
3
Department of Higher Mathematics, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg 197376, Russia
4
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
5
Department of Mathematics and Mechanics, Saint Petersburg State University, Saint Petersburg 199004, Russia
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Computational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: School of Pharmaceutical Sciences, University de Geneva, CMU, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland.
Academic Editor: Hunter N. B. Moseley
Metabolites 2021, 11(10), 693; https://doi.org/10.3390/metabo11100693
Received: 13 September 2021 / Revised: 1 October 2021 / Accepted: 1 October 2021 / Published: 11 October 2021
(This article belongs to the Special Issue Computational Methods for Secondary Metabolite Discovery)
Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes. View Full-Text
Keywords: natural products; nonribosomal peptides; genome mining; biosynthetic gene clusters; bioinformatics; mass spectrometry; software; machine learning natural products; nonribosomal peptides; genome mining; biosynthetic gene clusters; bioinformatics; mass spectrometry; software; machine learning
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MDPI and ACS Style

Kunyavskaya, O.; Tagirdzhanov, A.M.; Caraballo-Rodríguez, A.M.; Nothias, L.-F.; Dorrestein, P.C.; Korobeynikov, A.; Mohimani, H.; Gurevich, A. Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides. Metabolites 2021, 11, 693. https://doi.org/10.3390/metabo11100693

AMA Style

Kunyavskaya O, Tagirdzhanov AM, Caraballo-Rodríguez AM, Nothias L-F, Dorrestein PC, Korobeynikov A, Mohimani H, Gurevich A. Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides. Metabolites. 2021; 11(10):693. https://doi.org/10.3390/metabo11100693

Chicago/Turabian Style

Kunyavskaya, Olga, Azat M. Tagirdzhanov, Andrés Mauricio Caraballo-Rodríguez, Louis-Félix Nothias, Pieter C. Dorrestein, Anton Korobeynikov, Hosein Mohimani, and Alexey Gurevich. 2021. "Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides" Metabolites 11, no. 10: 693. https://doi.org/10.3390/metabo11100693

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