Evidence map›Paper›PMID 34677408›Full record

ArticleMetabolites2021

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

Olga Kunyavskaya, Azat M Tagirdzhanov, Andrés Mauricio Caraballo-Rodríguez, Louis-Félix Nothias, Pieter C Dorrestein, Anton Korobeynikov, Hosein Mohimani, Alexey Gurevich

Abstract read
In one paragraph

Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Peptidomics.Nature reviews. Methods primers · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Olga KunyavskayaCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg 199004, Russia.ORCID 0000-0001-9544-6344
Azat M TagirdzhanovCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg 199004, Russia.ORCID 0000-0002-6185-0821
Andrés Mauricio Caraballo-RodríguezCollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0001-5499-2728
Louis-Félix NothiasCollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0001-6711-6719
Pieter C DorresteinCollaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0002-3003-1030
Anton KorobeynikovCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg 199004, Russia.ORCID 0000-0002-2937-9259
Hosein MohimaniComputational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0003-3980-2505
Alexey GurevichCenter for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg 199004, Russia.ORCID 0000-0002-5855-3519

Funding

Gordon and Betty Moore Foundation GBMF7622National Institutes of Health 1DP2GM137413-01National Institutes of Health DP2GM137413National Institutes of Health P41 GM103484National Institutes of Health R01 GM107550National Science Foundation DBI2117640Russian Foundation for Basic Research 19-34-51017the Alfred P. Sloan Foundation a research fellowshipUnited States Department of Energy DE-SC0021340
6 · The paper itself

Abstract

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

Indexed as

bioinformaticsbiosynthetic gene clustersgenome miningmachine learningmass spectrometrynatural productsnonribosomal peptidessoftware

Identifiers

PMID34677408
PMCPMC8541647

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.