Structural basis of ribosomal peptide macrocyclization in plants

  1. Joel Haywood
  2. Jason W Schmidberger
  3. Amy M James
  4. Samuel G Nonis
  5. Kirill V Sukhoverkov
  6. Mikael Elias
  7. Charles S Bond
  8. Joshua S Mylne  Is a corresponding author
  1. The University of Western Australia, Australia
  2. University of Minnesota, United States

Abstract

Constrained, cyclic peptides encoded by plant genes represent a new generation of drug leads. Evolution has repeatedly recruited the Cys-protease asparaginyl endopeptidase (AEP) to perform their head-to-tail ligation. These macrocyclization reactions use the substrates amino terminus instead of water to deacylate, so a peptide bond is formed. How solvent-exposed plant AEPs macrocyclize is poorly understood. Here we present the crystal structure of an active plant AEP from the common sunflower, Helianthus annuus. The active site contained electron density for a tetrahedral intermediate with partial occupancy that predicted a binding mode for peptide macrocyclization. By substituting catalytic residues we could alter the ratio of cyclic to acyclic products. Moreover, we showed AEPs from other species lacking cyclic peptides can perform macrocyclization under favorable pH conditions. This structural characterization of AEP presents a logical framework for engineering superior enzymes that generate macrocyclic peptide drug leads.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Joel Haywood

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  2. Jason W Schmidberger

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  3. Amy M James

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  4. Samuel G Nonis

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Kirill V Sukhoverkov

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  6. Mikael Elias

    Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, St Paul, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Charles S Bond

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  8. Joshua S Mylne

    School of Molecular Sciences, The University of Western Australia, Perth, Australia
    For correspondence
    joshua.mylne@uwa.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4957-6388

Funding

Australian Research Council (FT120100013)

  • Joshua S Mylne

Australian Research Council (DP160100107)

  • Joshua S Mylne

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2018, Haywood et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,127
    views
  • 505
    downloads
  • 49
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Joel Haywood
  2. Jason W Schmidberger
  3. Amy M James
  4. Samuel G Nonis
  5. Kirill V Sukhoverkov
  6. Mikael Elias
  7. Charles S Bond
  8. Joshua S Mylne
(2018)
Structural basis of ribosomal peptide macrocyclization in plants
eLife 7:e32955.
https://doi.org/10.7554/eLife.32955

Share this article

https://doi.org/10.7554/eLife.32955

Further reading

    1. Structural Biology and Molecular Biophysics
    Julia Belyaeva, Matthias Elgeti
    Review Article

    Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure–function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.

    1. Structural Biology and Molecular Biophysics
    Yao Chi Chen, Karen Sargsyan ... Carmay Lim
    Research Article

    Experimental detection of residues critical for protein–protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein–protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).