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Computational approaches to natural product discovery

Nature Chemical Biology volume 11, pages 639–648 (2015)Cite this article

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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.

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Figure 1: The role of computation in natural product discovery.
Figure 2: Strategies for identifying BGCs.
Figure 3: Grouping BCGs into families.
Figure 4: Big data challenges for biosynthesis.

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Acknowledgements

We are indebted to P. Cimermancic, M. Donia and members of the Fischbach group for helpful conversations. This work was supported by a Rubicon grant of the Netherlands Organization for Scientific Research (NWO; Rubicon 825.13.001) to M.H.M. and by grants from the W.M. Keck Foundation (M.A.F.), the David and Lucile Packard Foundation (M.A.F.), the Glenn Foundation (M.A.F.), the Burroughs Wellcome Fund Investigators in the Pathogenesis of Infectious Disease program (M.A.F.), the Program for Breakthrough Biomedical Research (M.A.F.), US Defense Advanced Research Projects Agency (DARPA) award HR0011-12-C-0067 (M.A.F.) and US National Institutes of Health grants OD007290, AI101018, GM081879 and DK101674 (M.A.F.).

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  1. Bioinformatics Group, Wageningen University, Wageningen, the Netherlands

    Marnix H Medema

  2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA

    Michael A Fischbach

  3. California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California, USA

    Michael A Fischbach

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Correspondence to Marnix H Medema or Michael A Fischbach.

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M.A.F. is on the scientific advisory boards of NGM Biopharmaceuticals and Warp Drive Bio.

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Medema, M., Fischbach, M. Computational approaches to natural product discovery. Nat Chem Biol 11, 639–648 (2015). https://doi.org/10.1038/nchembio.1884

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