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Journal ArticleDOI

The confluence of big data and evolutionary genome mining for the discovery of natural products.

TLDR
This review covers literature between 2003-2021 and highlights examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes.
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This article is published in Natural Product Reports.The article was published on 2021-11-17. It has received 21 citations till now.

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A Systematic Computational Analysis of Biosynthetic Gene Cluster Evolution: Lessons for Engineering Biosynthesis - eScholarship

TL;DR: By performing a systematic computational analysis of BGC evolution, this work derives evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules.
Journal ArticleDOI

Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes

TL;DR: The authors analyzed ~170,000 bacterial genomes and ~47,000 metagenome assembled genomes using a modified BiG-SLiCE and the new clust-o-matic algorithm.
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Microbiome composition modulates secondary metabolism in a multispecies bacterial community

TL;DR: Results from this model community show that bacterial BGC expression and chemical output depend on the identity and biosynthetic capacity of coculture partners, suggesting community composition and microbiome interactions may shape the regulation of secondary metabolism in nature.
Journal ArticleDOI

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery

TL;DR: Genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data are discussed, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities.
Journal ArticleDOI

Integrated Metabolomic–Genomic Workflows Accelerate Microbial Natural Product Discovery

TL;DR: This work considers innovative approaches which have led to prioritization of strain targets and have mitigated rediscovery rates, and discusses integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure.
References
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Journal ArticleDOI

Drivers of metabolic diversification: how dynamic genomic neighbourhoods generate new biosynthetic pathways in the Brassicaceae

TL;DR: The results indicate that plant genomes are remarkably plastic, and that dynamic GNs generate new biosynthetic pathways in different Brassicaceae lineages by shuffling the genes encoding a core palette of triterpene‐diversifying enzymes, presumably in response to strong environmental selection pressure.
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Bacterial natural product biosynthetic domain composition in soil correlates with changes in latitude on a continent-wide scale

TL;DR: Of the factors assessed, changes in latitude were found to correlate most consistently with changes in biosynthetic domain composition on a continent-wide scale, and the identification of a latitudinal basis for differences in soil metagenome biosynthesis domain compositions should help guide future natural product discovery efforts.
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Soil bacterial populations are shaped by recombination and gene-specific selection across a grassland meadow.

TL;DR: The results indicate that recombination and gene-specific selection commonly shape genetic variation in several understudied soil bacterial lineages.
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EvoMining reveals the origin and fate of natural product biosynthetic enzymes

TL;DR: By defining the origin and fate of enzymes, EvoMining complements traditional genome-mining approaches as an unbiased strategy and opens the door to gaining insights into the evolution of NP biosynthesis.
Journal ArticleDOI

Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity.

TL;DR: A tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl- CoA synthetase and enoyl-CoA hydratase genes is described and insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity are revealed.
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