Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A new genome-mining tool redefines the lasso peptide biosynthetic landscape

Nature Chemical Biology volume 13, pages 470–478 (2017)Cite this article

Subjects

Abstract

Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining of RiPP data difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden-Markov-model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physicochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping to date of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including one with an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides and, more broadly, provide a framework for future genome-mining efforts.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Ribosomal natural product (RiPP) biosynthesis and overview of RODEO.
Figure 2: Phylogenetic map of all identified lasso peptides.
Figure 3: Lasso peptide sequence analysis.
Figure 4: SSNs of lasso cyclase protein.
Figure 5: Lasso peptides discovered via RODEO-based prioritization.
Figure 6: Citrulassin, a rare example of bacterial PAD activity.

Similar content being viewed by others

Accession codes

Primary accessions

GenBank/EMBL/DDBJ

Protein Data Bank

Referenced accessions

GenBank/EMBL/DDBJ

Protein Data Bank

References

  1. Newman, D.J. & Cragg, G.M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod. 79, 629–661 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Winter, J.M., Behnken, S. & Hertweck, C. Genomics-inspired discovery of natural products. Curr. Opin. Chem. Biol. 15, 22–31 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Medema, M.H. et al. Minimum Information about a Biosynthetic Gene cluster. Nat. Chem. Biol. 11, 625–631 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Weber, T. et al. antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters. Nucleic Acids Res. 43, W237–W243 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Tietz, J.I. & Mitchell, D.A. Using genomics for natural product structure elucidation. Curr. Top. Med. Chem. 16, 1645–1694 (2016).

    CAS  PubMed  Google Scholar 

  6. Stachelhaus, T., Mootz, H.D. & Marahiel, M.A. The specificity-conferring code of adenylation domains in nonribosomal peptide synthetases. Chem. Biol. 6, 493–505 (1999).

    CAS  PubMed  Google Scholar 

  7. Skinnider, M.A. et al. Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM). Nucleic Acids Res. 43, 9645–9662 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Cimermancic, P. et al. Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. Cell 158, 412–421 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Doroghazi, J.R. et al. A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat. Chem. Biol. 10, 963–968 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Arnison, P.G. et al. Ribosomally synthesized and post-translationally modified peptide natural products: overview and recommendations for a universal nomenclature. Nat. Prod. Rep. 30, 108–160 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Burkhart, B.J., Hudson, G.A., Dunbar, K.L. & Mitchell, D.A. A prevalent peptide-binding domain guides ribosomal natural product biosynthesis. Nat. Chem. Biol. 11, 564–570 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. van Heel, A.J., de Jong, A., Montalbán-López, M., Kok, J. & Kuipers, O.P. BAGEL3: Automated identification of genes encoding bacteriocins and (non-)bactericidal posttranslationally modified peptides. Nucleic Acids Res. 41, W448–W453 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. Hegemann, J.D., Zimmermann, M., Xie, X. & Marahiel, M.A. Lasso peptides: an intriguing class of bacterial natural products. Acc. Chem. Res. 48, 1909–1919 (2015).

    CAS  PubMed  Google Scholar 

  14. Al Toma, R.S. et al. Site-directed and global incorporation of orthogonal and isostructural noncanonical amino acids into the ribosomal lasso peptide capistruin. ChemBioChem 16, 503–509 (2015).

    CAS  PubMed  Google Scholar 

  15. Pan, S.J., Rajniak, J., Maksimov, M.O. & Link, A.J. The role of a conserved threonine residue in the leader peptide of lasso peptide precursors. Chem. Commun. 48, 1880–1882 (2012).

    CAS  Google Scholar 

  16. Zong, C., Maksimov, M.O. & Link, A.J. Construction of lasso peptide fusion proteins. ACS Chem. Biol. 11, 61–68 (2016).

    CAS  PubMed  Google Scholar 

  17. Kans, J. Entrez Direct: E-utilities on the UNIX Command Line (National Center for Biotechnology Information, 2010).

  18. Finn, R.D. et al. HMMER web server: 2015 update. Nucleic Acids Res. 43 W1, W30–W38 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Finn, R.D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).

    CAS  PubMed  Google Scholar 

  20. Maksimov, M.O., Pelczer, I. & Link, A.J. Precursor-centric genome-mining approach for lasso peptide discovery. Proc. Natl. Acad. Sci. USA 109, 15223–15228 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Libbrecht, M.W. & Noble, W.S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Röttig, M. et al. NRPSpredictor2—a web server for predicting NRPS adenylation domain specificity. Nucleic Acids Res. 39, W362–W367 (2011).

    PubMed  PubMed Central  Google Scholar 

  23. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  24. Bailey, T.L., Johnson, J., Grant, C.E. & Noble, W.S. The MEME Suite. Nucleic Acids Res. 43, W39–W49 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Blin, K., Medema, M.H., Kottmann, R., Lee, S.Y. & Weber, T. The antiSMASH database, a comprehensive database of microbial secondary metabolite biosynthetic gene clusters. Nucleic Acids Res. 45, D555–D559 (2017).

    CAS  PubMed  Google Scholar 

  26. Skinnider, M.A. et al. Genomic charting of ribosomally synthesized natural product chemical space facilitates targeted mining. Proc. Natl. Acad. Sci. USA 113, E6343–E6351 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Gerlt, J.A. et al. Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST): a web tool for generating protein sequence similarity networks. Biochim. Biophys. Acta 1854, 1019–1037 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Maksimov, M.O., Pan, S.J. & James Link, A. Lasso peptides: structure, function, biosynthesis, and engineering. Nat. Prod. Rep. 29, 996–1006 (2012).

    CAS  PubMed  Google Scholar 

  29. Zimmermann, M., Hegemann, J.D., Xie, X.L. & Marahiel, M.A. Characterization of caulonodin lasso peptides revealed unprecedented N-terminal residues and a precursor motif essential for peptide maturation. Chem. Sci. 5, 4032–4043 (2014).

    CAS  Google Scholar 

  30. Metelev, M. et al. Structure, bioactivity, and resistance mechanism of streptomonomicin, an unusual lasso peptide from an understudied halophilic actinomycete. Chem. Biol. 22, 241–250 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Hegemann, J.D. et al. The ring residue proline 8 is crucial for the thermal stability of the lasso peptide caulosegnin II. Mol. Biosyst. 12, 1106–1109 (2016).

    CAS  PubMed  Google Scholar 

  32. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

  33. Hopf, T.A. et al. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3, e03430 (2014).

    PubMed Central  Google Scholar 

  34. Ovchinnikov, S., Kamisetty, H. & Baker, D. Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information. eLife 3, e02030 (2014).

    PubMed  PubMed Central  Google Scholar 

  35. Balakrishnan, S., Kamisetty, H., Carbonell, J.G., Lee, S.I. & Langmead, C.J. Learning generative models for protein fold families. Proteins 79, 1061–1078 (2011).

    CAS  PubMed  Google Scholar 

  36. Koehnke, J. et al. Structural analysis of leader peptide binding enables leader-free cyanobactin processing. Nat. Chem. Biol. 11, 558–563 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Maksimov, M.O. & Link, A.J. Discovery and characterization of an isopeptidase that linearizes lasso peptides. J. Am. Chem. Soc. 135, 12038–12047 (2013).

    CAS  PubMed  Google Scholar 

  38. Weber, W., Fischli, W., Hochuli, E., Kupfer, E. & Weibel, E.K. Anantin--a peptide antagonist of the atrial natriuretic factor (ANF). I. Producing organism, fermentation, isolation and biological activity. J. Antibiot. 44, 164–171 (1991).

    CAS  Google Scholar 

  39. Xie, X. & Marahiel, M.A. NMR as an effective tool for the structure determination of lasso peptides. ChemBioChem 13, 621–625 (2012).

    CAS  PubMed  Google Scholar 

  40. Ogawa, T. et al. RES-701-2, -3 and -4, novel and selective endothelin type B receptor antagonists produced by Streptomyces sp. I. Taxonomy of producing strains, fermentation, isolation, and biochemical properties. J. Antibiot. 48, 1213–1220 (1995).

    CAS  Google Scholar 

  41. Gavrish, E. et al. Lassomycin, a ribosomally synthesized cyclic peptide, kills mycobacterium tuberculosis by targeting the ATP-dependent protease ClpC1P1P2. Chem. Biol. 21, 509–518 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Goulas, T. et al. Structure and mechanism of a bacterial host-protein citrullinating virulence factor, Porphyromonas gingivalis peptidylarginine deiminase. Sci. Rep. 5, 11969 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Markowitz, V.M. et al. IMG 4 version of the integrated microbial genomes comparative analysis system. Nucleic Acids Res. 42, D560–D567 (2014).

    CAS  PubMed  Google Scholar 

  44. Fong, C., Rohmer, L., Radey, M., Wasnick, M. & Brittnacher, M.J. PSAT: a web tool to compare genomic neighborhoods of multiple prokaryotic genomes. BMC Bioinformatics 9, 170 (2008).

    PubMed  PubMed Central  Google Scholar 

  45. Molloy, E.M., Tietz, J.I., Blair, P.M. & Mitchell, D.A. Biological characterization of the hygrobafilomycin antibiotic JBIR-100 and bioinformatic insights into the hygrolide family of natural products. Bioorg. Med. Chem. 24, 6276–6290 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Li, Y. et al. Characterization of sviceucin from streptomyces provides insight into enzyme exchangeability and disulfide bond formation in lasso peptides. ACS Chem. Biol. 10, 2641–2649 (2015).

    CAS  PubMed  Google Scholar 

  47. McGraw, W.T., Potempa, J., Farley, D. & Travis, J. Purification, characterization, and sequence analysis of a potential virulence factor from Porphyromonas gingivalis, peptidylarginine deiminase. Infect. Immun. 67, 3248–3256 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Gabarrini, G. et al. The peptidylarginine deiminase gene is a conserved feature of Porphyromonas gingivalis. Sci. Rep. 5, 13936 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Hildebrand, A., Remmert, M., Biegert, A. & Söding, J. Fast and accurate automatic structure prediction with HHpred. Proteins 77 (Suppl. 9), 128–132 (2009).

    CAS  PubMed  Google Scholar 

  51. Letunic, I. & Bork, P. Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res. 39, W475–W478 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Su, G., Morris, J.H., Demchak, B. & Bader, G.D. Biological network exploration with Cytoscape 3. Curr. Protoc. Bioinformatics 47, 8.13.1–8.1324 (2014).

    Google Scholar 

  53. Kohl, M., Wiese, S. & Warscheid, B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol. Biol. 696, 291–303 (2011).

    CAS  PubMed  Google Scholar 

  54. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Crooks, G.E., Hon, G., Chandonia, J.M. & Brenner, S.E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Edgar, R.C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Eliot, A.C. et al. Cloning, expression, and biochemical characterization of Streptomyces rubellomurinus genes required for biosynthesis of antimalarial compound FR900098. Chem. Biol. 15, 765–770 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Schwieters, C.D., Kuszewski, J.J. & Clore, G.M. Using Xplor-NIH for NMR molecular structure determination. Prog. Nucl. Magn. Reson. Spectrosc. 48, 47–62 (2006).

    CAS  Google Scholar 

Download references

Acknowledgements

We thank L. Zhu (University of Illinois Urbana–Champaign) and B. Ramirez (University of Illinois Chicago) for NMR assistance. We acknowledge C. Cox, K. Choe (University of Illinois Urbana–Champaign), and N. Tietz for valuable computational and programming input. This work was supported in part by a NIH Director's New Innovator Award Program (DP2 OD008463 to D.A.M.), the David and Lucile Packard Fellowship for Science and Engineering (to D.A.M.), the Robert C. and Carolyn J. Springborn Endowment (to J.I.T.), and ACS Division of Medicinal Chemistry Predoctoral Fellowships (to P.M.B. and J.I.T.). C.J.S. is a member of the NIH Chemistry–Biology Interface Training Program (Grant NRSA 1-T32-GM070421). The Bruker UltrafleXtreme MALDI TOF/TOF mass spectrometer was purchased in part with a grant from the National Center for Research Resources, National Institutes of Health (S10 RR027109 A). The 900 MHz NMR spectrometer was purchased with funds provided by GM068944.

Author information

Author notes

  1. Jonathan I Tietz and Christopher J Schwalen: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA

    Jonathan I Tietz, Christopher J Schwalen, Parth S Patel, Tucker Maxson, Patricia M Blair, Hua-Chia Tai, Uzma I Zakai & Douglas A Mitchell

  2. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA

    Douglas A Mitchell

  3. Department of Microbiology, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA

    Douglas A Mitchell

Authors
  1. Jonathan I Tietz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher J Schwalen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parth S Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tucker Maxson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patricia M Blair
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hua-Chia Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Uzma I Zakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Douglas A Mitchell
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.I.T. and C.J.S. contributed equally to this work. Experiments were designed by J.I.T., C.J.S. and D.A.M. and were performed by J.I.T., C.J.S., T.M., P.M.B., H.-C.T. and U.I.Z. J.I.T., P.S.P. and C.J.S. wrote the code. The manuscript was written by J.I.T., C.J.S., and D.A.M.

Corresponding author

Correspondence to Douglas A Mitchell.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Tables 1–11, Supplementary Figures 1–15 and Supplementary Notes 1–3 (PDF 9591 kb)

Supplementary Dataset 1

Spreadsheet containing all RODEO-derived information on lasso peptide precursors, BGCs, co-occurrence. (XLSX 14569 kb)

Supplementary Dataset 2

Cytoscape-readable file containing individual sequence similarity networks for the lasso precursors, lasso cyclase, leader peptidase, and RRE (PqqD) proteins. (ZIP 20183 kb)

Supplementary Dataset 3

High-resolution PDF showing a Circos diagram of all RODEO-identified lasso peptide BGCs with annotations indicating co-occurring Pfam information. (PDF 28576 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tietz, J., Schwalen, C., Patel, P. et al. A new genome-mining tool redefines the lasso peptide biosynthetic landscape. Nat Chem Biol 13, 470–478 (2017). https://doi.org/10.1038/nchembio.2319

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio.2319

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing