The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Computational approaches to natural product discovery
Open accessAbstract
From 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 long-standing questions in microbial ecology regarding the roles of metabolites in interspecies interactions.
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Cited by
Blin, K., Shaw, S., Augustijn, H. E., Reitz, Z. L., Biermann, F., Alanjary, M., Fetter, A., Terlouw, B. R., Metcalf, W. W., Helfrich, E. J. N., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad344 · Cited by 2,290
Self-citationCounting on natural products for drug design
Rodrigues, T., Reker, D., Schneider, P., & Schneider, G. (2016) · Nature Chemistry · doi:10.1038/nchem.2479 · Cited by 1,250
Homeostatic Immunity and the Microbiota
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antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification
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Metadata
- Authors
- Medema, M. H., & Fischbach, M. A.
- Venue
- Nature Chemical Biology
- Published
- 2015-08-18
- Volume
- 11
- Issue
- 9
- Pages
- 639-648
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- OpenAlex
- W1202680985
- Cited by
- 493
- References
- 89
- FWCI
- 29.82
- Type
- journal article
- Language
- en
- Open access
- green
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