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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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Citations
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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

A roadmap for natural product discovery based on large-scale genomics and metabolomics

TL;DR: Extrapolation from the 830-genome dataset reveals that Actinobacteria encode hundreds of thousands of future drug leads, while the strong correlation between phylogeny and GCFs frames a roadmap to efficiently access them.
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Natural products – a simple model to explain chemical diversity

TL;DR: A simple evolutionary model is presented which explains why organisms produce so many natural products, why so many have low biological activity, why enzymes involved in natural product synthesis have the properties they do and why natural product metabolism is shaped as it is.
Journal ArticleDOI

Exploiting the mosaic structure of trans-acyltransferase polyketide synthases for natural product discovery and pathway dissection.

TL;DR: It is shown that trans-AT PKSs evolved in a fundamentally different fashion from cis-AT systems, through horizontal recruitment and assembly of substrate-specific ketosynthase (KS) domains, and will facilitate both the discovery of novel polyketides by genome mining and the extraction of chemical information from short trans- AT PCR products, using metagenomic DNA of marine sponges.
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