ArticleInternational journal of molecular sciences2023
A Comprehensive Self-Resistance Gene Database for Natural-Product Discovery with an Application to Marine Bacterial Genome Mining.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Self-Resistance as a Functional Beacon: Target-Directed Microbial Genome Mining from Classical Discovery to Automated Pipelines.Microorganisms · 2026Review
- Aquatic-derived Streptomyces cavourensis BA1 as a promising source of antibacterial metabolites against multidrug-resistant gram-negative pathogens.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2026Article
- Advances in tools, strategies, and applications of mining of microbial genomes for novel antimicrobials: a comprehensive review.Folia microbiologica · 2026Review
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In the world of microorganisms, the biosynthesis of natural products in secondary metabolism and the self-resistance of the host always occur together and complement each other. Identifying resistance genes from biosynthetic gene clusters (BGCs) helps us understand the self-defense mechanism and predict the biological activity of natural products synthesized by microorganisms. However, a comprehensive database of resistance genes is still lacking, which hinders natural product annotation studies in large-scale genome mining. In this study, we compiled a resistance gene database (RGDB) by scanning the four available databases: CARD, MIBiG, NCBIAMR, and UniProt. Every resistance gene in the database was annotated with resistance mechanisms and possibly involved chemical compounds, using manual annotation and transformation from the resource databases. The RGDB was applied to analyze resistance genes in 7432 BGCs in 1390 genomes from a marine microbiome project. Our calculation showed that the RGDB successfully identified resistance genes for more than half of the BGCs, suggesting that the database helps prioritize BGCs that produce biologically active natural products.
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Registered trials
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