Computational approaches to natural product discovery

By Marnix H. Medema, Michael A. Fischbach. Nature Chemical Biology, 2015

440 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/2595dcfd8ff6ef2f433f276fa6c24206465d6e57

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Citations in the corpus

Homeostatic immunity and the microbiota (2017, neutral): “Uncovering these pathways and the microbiota-derived molecules (Donia and Fischbach, 2015; Medema and Fischbach, 2015) involved in these processes may allow for the development of novel classes of adjuvants with potent potential for the control of infection as well as for the promotion of…”
antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification (2017, methodology): “To broaden its applicability, the RODEO algorithm was extended to perform precursor prediction not only for lasso peptides, but also for thiopeptides, class I lanthipep-tides and sactipeptides (see Supplementary Text 1 and Figures S2–4).”
A computational framework to explore large-scale biosynthetic diversity (2019, positive): “Moreover, in many cases, the chemical structures of their products can be predicted to a certain extent, based on the analysis and biosynthetic logic of the enzymes encoded in a BGC and their similarity to known counterparts 9 .”
A deep learning genome-mining strategy for biosynthetic gene cluster prediction (2019, background): “Numerous bioinformatics tools have leveraged the increasingly abundant genomic data to facilitate natural product genome mining (12).”
antiSMASH 6.0: improving cluster detection and comparison capabilities (2021, background): “Depending on the research question and the desired sensitivity vs. specificity trade-off, there can be a value in preferring one cluster detection over the other (see (8,35) for a discussion).”
Artificial Intelligence For Natural Product Drug Discovery (2023, methodology): “Although these approaches are successful at detecting known BGC classes, they are less proficient at identifying novel types of BGC or unclustered pathways 13,14 .”
Natural product discovery: past, present, and future (2016, background): “It is now estimated that 10 % of SMGCs are expressed in sufficient quantities to be observed under routine fermentation analyses, and the others require special conditions or genetic manipulations to reveal their products [21, 89, 133, 1 3 134].”
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery (2018, background): “Technological advances, such as the development of new analytical and bio-informatic techniques, will aid the design of new structures, the synthesis of these new compounds and the biological testing of such compounds [73,74].”