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Polyketide and nonribosomal peptide retro-biosynthesis and global gene cluster matching

Nature Chemical Biology volume 12, pages 1007–1014 (2016)Cite this article

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Abstract

Polyketides (PKs) and nonribosomal peptides (NRPs) are profoundly important natural products, forming the foundations of many therapeutic regimes. Decades of research have revealed over 11,000 PK and NRP structures, and genome sequencing is uncovering new PK and NRP gene clusters at an unprecedented rate. However, only ∼10% of PK and NRPs are currently associated with gene clusters, and it is unclear how many of these orphan gene clusters encode previously isolated molecules. Therefore, to efficiently guide the discovery of new molecules, we must first systematically de-orphan emergent gene clusters from genomes. Here we provide to our knowledge the first comprehensive retro-biosynthetic program, generalized retro-biosynthetic assembly prediction engine (GRAPE), for PK and NRP families and introduce a computational pipeline, global alignment for natural products cheminformatics (GARLIC), to uncover how observed biosynthetic gene clusters relate to known molecules, leading to the identification of gene clusters that encode new molecules.

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Figure 1: Historical perspective of microbial polyketide and nonribosomal peptide natural-product discovery and associated genetic information.
Figure 2: Workflow of GRAPE.
Figure 3: Matching algorithm of GARLIC and examples of natural products matched.
Figure 4: Matching results of the test clusters (171 NRP, type 1 PK and hybrid PK-NRP) to in-house compound database (48,222 microbial natural products that can be processed via GRAPE).
Figure 5: Discovery of gene clusters for orphaned microbial natural products, and identification of new microbial natural products using the GARLIC pipeline.

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Acknowledgements

This work was funded through an Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery grant (RGPIN 371576-2014) (N.A.M.) and a Joint Programme Initiative on Antimicrobial Resistance funded through the Canadian Institutes of Health Research (CIHR) (grant 138739) (N.A.M.). C.W.J. is funded through a CIHR Doctoral Research Award. N.A.M. is supported by the Canada Research Chairs Program (grant 950228183). We thank J. Cao for rendering trees, A. Luo for curating sugar genes and structures, and B. Furman for valuable communications.

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Author notes

  1. Chris A Dejong, Gregory M Chen and Haoxin Li: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada

    Chris A Dejong, Gregory M Chen, Haoxin Li, Chad W Johnston, Mclean R Edwards, Philip N Rees, Michael A Skinnider, Andrew L H Webster & Nathan A Magarvey

  2. Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada

    Chris A Dejong, Gregory M Chen, Haoxin Li, Chad W Johnston, Mclean R Edwards, Philip N Rees, Michael A Skinnider, Andrew L H Webster & Nathan A Magarvey

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Contributions

C.A.D. and G.M.C. developed GRAPE and GARLIC, devised scoring strategies, contributed to study design, and wrote the manuscript. H.L. consulted on GRAPE and GARLIC's logic, devised scoring strategies, curated data sets, contributed to study design, and wrote the manuscript. C.W.J. isolated compounds and characterized structures. M.R.E. revised GARLIC, and designed and performed the optimization analysis of GARLIC scoring. M.A.S. developed PRISM, contributed to study design, and completed analysis for Figure 1. P.N.R. curated data sets, and contributed to study design. A.L.H.W. curated data sets. N.A.M. contributed to study design and wrote the manuscript.

Corresponding author

Correspondence to Nathan A Magarvey.

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Competing interests

N.A.M. is a founder of Adapsyn Bioscience; H.L., C.A.D. and M.A.S. are consultants of Adapsyn Bioscience. Adapsyn Bioscience has licensed some aspects of the technologies relating to GARLIC.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–16, Supplementary Tables 1–7 and Supplementary Note. (PDF 6564 kb)

Supplementary Dataset

Names and abbreviations of substrates used in the GARLIC pipeline. (XLSX 20 kb)

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Dejong, C., Chen, G., Li, H. et al. Polyketide and nonribosomal peptide retro-biosynthesis and global gene cluster matching. Nat Chem Biol 12, 1007–1014 (2016). https://doi.org/10.1038/nchembio.2188

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