1. Computational and Systems Biology
Download icon

Origin of a folded repeat protein from an intrinsically disordered ancestor

  1. Hongbo Zhu
  2. Edgardo Sepulveda
  3. Marcus D Hartmann
  4. Manjunatha Kogenaru
  5. Astrid Ursinus
  6. Eva Sulz
  7. Reinhard Albrecht
  8. Murray Coles
  9. Jörg Martin
  10. Andrei N Lupas  Is a corresponding author
  1. Max Planck Institute for Developmental Biology, Germany
  2. Imperial College London, United Kingdom
Research Article
  • Cited 21
  • Views 2,263
  • Annotations
Cite this article as: eLife 2016;5:e16761 doi: 10.7554/eLife.16761

Abstract

Repetitive proteins are thought to have arisen through the amplification of subdomain-sized peptides. Many of these originated in a non-repetitive context as cofactors of RNA-based replication and catalysis, and required the RNA to assume their active conformation. In search of the origins of one of the most widespread repeat protein families, the tetratricopeptide repeat (TPR), we identified several potential homologs of its repeated helical hairpin in non-repetitive proteins, including the putatively ancient ribosomal protein S20 (RPS20), which only becomes structured in the context of the ribosome. We evaluated the ability of the RPS20 hairpin to form a TPR fold by amplification and obtained structures identical to natural TPRs for variants with 2-5 point mutations per repeat. The mutations were neutral in the parent organism, suggesting that they could have been sampled in the course of evolution. TPRs could thus have plausibly arisen by amplification from an ancestral helical hairpin.

Article and author information

Author details

  1. Hongbo Zhu

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Edgardo Sepulveda

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2413-8261
  3. Marcus D Hartmann

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6937-5677
  4. Manjunatha Kogenaru

    Department of Life Sciences, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6570-7857
  5. Astrid Ursinus

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Eva Sulz

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  7. Reinhard Albrecht

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  8. Murray Coles

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  9. Jörg Martin

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  10. Andrei N Lupas

    Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
    For correspondence
    andrei.lupas@tuebingen.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1959-4836

Funding

Max-Planck-Gesellschaft

  • Hongbo Zhu
  • Edgardo Sepulveda
  • Marcus D Hartmann
  • Manjunatha Kogenaru
  • Astrid Ursinus
  • Eva Sulz
  • Reinhard Albrecht
  • Murray Coles
  • Jörg Martin
  • Andrei N Lupas

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

Reviewing Editor

  1. Nir Ben-Tal, Tel Aviv University, Israel

Publication history

  1. Received: April 8, 2016
  2. Accepted: September 9, 2016
  3. Accepted Manuscript published: September 13, 2016 (version 1)
  4. Version of Record published: October 21, 2016 (version 2)

Copyright

© 2016, Zhu 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

  • 2,263
    Page views
  • 509
    Downloads
  • 21
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

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)

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

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

Further reading

    1. Computational and Systems Biology
    2. Medicine
    Homa MohammadiPeyhani et al.
    Tools and Resources

    The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug–drug and drug–metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

    1. Computational and Systems Biology
    2. Microbiology and Infectious Disease
    Wellington Miranda S et al.
    Research Article Updated

    Many bacteria communicate with kin and coordinate group behaviors through a form of cell-cell signaling called acyl-homoserine lactone (AHL) quorum sensing (QS). In these systems, a signal synthase produces an AHL to which its paired receptor selectively responds. Selectivity is fundamental to cell signaling. Despite its importance, it has been challenging to determine how this selectivity is achieved and how AHL QS systems evolve and diversify. We hypothesized that we could use covariation within the protein sequences of AHL synthases and receptors to identify selectivity residues. We began by identifying about 6000 unique synthase-receptor pairs. We then used the protein sequences of these pairs to identify covariation patterns and mapped the patterns onto the LasI/R system from Pseudomonas aeruginosa PAO1. The covarying residues in both proteins cluster around the ligand-binding sites. We demonstrate that these residues are involved in system selectivity toward the cognate signal and go on to engineer the Las system to both produce and respond to an alternate AHL signal. We have thus demonstrated that covariation methods provide a powerful approach for investigating selectivity in protein-small molecule interactions and have deepened our understanding of how communication systems evolve and diversify.