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

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing app...

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Bibliographic Details
Published in:Marine drugs 2022-06, Vol.20 (6), p.398
Main Authors: Malit, Jessie James Limlingan, Leung, Hiu Yu Cherie, Qian, Pei-Yuan
Format: Article
Language:English
Subjects:
Antibiotics
bioactive compounds
Bioassays
Bioinformatics
Biological activity
Biological Products - metabolism
Biosynthesis
Biosynthetic Pathways - genetics
Chemical compounds
Computational Biology - methods
Computer applications
Enzymes
Gene clusters
Genes
Genetic engineering
genome mining
Genome, Bacterial
Genomes
Genomics
Laboratories
Machine learning
Metabolites
Mining
Multigene Family
Natural products
Peptides
Review
secondary metabolites
Software
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Summary:Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.
ISSN:1660-3397
1660-3397
DOI:10.3390/md20060398