DOI

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 prod-ucts. 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.

Original languageEnglish
Article number693
Number of pages20
JournalMetabolites
Volume11
Issue number10
DOIs
StatePublished - 11 Oct 2021

    Research areas

  • Bioinformatics, Biosynthetic gene clusters, Genome mining, Machine learning, Mass spectrometry, Natural products, Nonribosomal peptides, Software, software, MASS-SPECTROMETRY, natural products, mass spectrometry, DIVERSITY, ADENYLATION DOMAINS, RESOURCE, biosynthetic gene clusters, ANTIBIOTICS, NATURAL-PRODUCTS, machine learning, IDENTIFICATION, DATABASE SEARCH, PREDICTION, bioinformatics, POLYKETIDE, nonribosomal peptides, genome mining

    Scopus subject areas

  • Molecular Biology
  • Biochemistry
  • Endocrinology, Diabetes and Metabolism

ID: 87636242