ArticleProceedings of the National Academy of Sciences of the United States of America2020
DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.
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Who cites it
81 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Breaking the antimicrobial resistance dilemma: marine natural products, a treasure trove for the development of new anti-Marine life science & technology · 2026Review
- The human microbiome as a source of novel bioactive natural products: structures, bioactivities, and biosynthetic insights.Journal of natural medicines · 2026Review
- Genomic Insights of Biosynthetic Potential from Actinoalloteichus caeruleus LHW52806, a Promising Actinomycete for Natural Product Discovery.Biochemical genetics · 2026Article
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery.Nature communications · 2026Article
- Mining the Hidden Pharmacopeia: Fungal Endophytes, Natural Products, and the Rise of AI-Driven Drug Discovery.International journal of molecular sciences · 2026Review
- Article
- BGC-MAC and BGC-MAP: Attention-Based Models for Biosynthetic Gene Cluster Classification and Product Matching.Journal of chemical information and modeling · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- Mangrove cyanobacterial diversity as a source of bioactive natural products.Current research in microbial sciences · 2026Article
- Probiogenomics as a Computational Biotechnology Framework: Safety-Gated Genome Analytics for Candidate Probiotic Prioritization and Validation.Computational and structural biotechnology journal · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Peptide Arginases from Cryptic Pathways Install Ornithine Residues in Uncharacterized Members of Orphan RiPP Families.Chembiochem : a European journal of chemical biology · 2025Article
- Systematically investigating and identifying bacteriocins in the human gut microbiome.Cell genomics · 2025Article
- Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0.Briefings in bioinformatics · 2025Article
- Sequence modeling tools to decode the biosynthetic diversity of the human microbiome.mSystems · 2025Review
- Strategies Used for the Discovery of New Microbial Metabolites with Antibiotic Activity.Molecules (Basel, Switzerland) · 2025Review
- Multi-omics approaches: transforming the landscape of natural product isolation.Functional & integrative genomics · 2025Review
- Evolution-Guided Discovery of Antimycobacterial Triculamin-Like Lasso Peptides.Angewandte Chemie (International ed. in English) · 2025Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
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Abstract
Microbial natural products represent a rich resource of evolved chemistry that forms the basis for the majority of pharmacotherapeutics. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) are a particularly interesting class of natural products noted for their unique mode of biosynthesis and biological activities. Analyses of sequenced microbial genomes have revealed an enormous number of biosynthetic loci encoding RiPPs but whose products remain cryptic. In parallel, analyses of bacterial metabolomes typically assign chemical structures to only a minority of detected metabolites. Aligning these 2 disparate sources of data could provide a comprehensive strategy for natural product discovery. Here we present DeepRiPP, an integrated genomic and metabolomic platform that employs machine learning to automate the selective discovery and isolation of novel RiPPs. DeepRiPP includes 3 modules. The first, NLPPrecursor, identifies RiPPs independent of genomic context and neighboring biosynthetic genes. The second module, BARLEY, prioritizes loci that encode novel compounds, while the third, CLAMS, automates the isolation of their corresponding products from complex bacterial extracts. DeepRiPP pinpoints target metabolites using large-scale comparative metabolomics analysis across a database of 10,498 extracts generated from 463 strains. We apply the DeepRiPP platform to expand the landscape of novel RiPPs encoded within sequenced genomes and to discover 3 novel RiPPs, whose structures are exactly as predicted by our platform. By building on advances in machine learning technologies, DeepRiPP integrates genomic and metabolomic data to guide the isolation of novel RiPPs in an automated manner.
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