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Nat Chem Biol. 2015 Sep;11(9):639-48. doi: 10.1038/nchembio.1884.

Computational approaches to natural product discovery.

Author information

1
Bioinformatics Group, Wageningen University, Wageningen, the Netherlands.
2
1] Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA. [2] California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California, USA.

Abstract

Starting with the earliest Streptomyces genome sequences, the promise of natural product genome mining has been captivating: genomics and bioinformatics would transform compound discovery from an ad hoc pursuit to a high-throughput endeavor. Until recently, however, genome mining has advanced natural product discovery only modestly. Here, we argue that the development of algorithms to mine the continuously increasing amounts of (meta)genomic data will enable the promise of genome mining to be realized. We review computational strategies that have been developed to identify biosynthetic gene clusters in genome sequences and predict the chemical structures of their products. We then discuss networking strategies that can systematize large volumes of genetic and chemical data and connect genomic information to metabolomic and phenotypic data. Finally, we provide a vision of what natural product discovery might look like in the future, specifically considering longstanding questions in microbial ecology regarding the roles of metabolites in interspecies interactions.

PMID:
26284671
PMCID:
PMC5024737
DOI:
10.1038/nchembio.1884
[Indexed for MEDLINE]
Free PMC Article

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