1. Evolutionary Biology
  2. Structural Biology and Molecular Biophysics
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Evolutionary pathways of repeat protein topology in bacterial outer membrane proteins

  1. Meghan Whitney Franklin
  2. Sergey Nepomnyachyi
  3. Ryan Feehan
  4. Nir Ben-Tal
  5. Rachel Kolodny
  6. Joanna SG Slusky  Is a corresponding author
  1. University of Kansas, United States
  2. Tel Aviv University, Israel
  3. University of Haifa, Israel
Research Article
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Cite this article as: eLife 2018;7:e40308 doi: 10.7554/eLife.40308

Abstract

Outer membrane proteins (OMPs) are the proteins in the surface of Gram-negative bacteria. These proteins have diverse functions but a single topology: the β-barrel. Sequence analysis has suggested that this common fold is a β-hairpin repeat protein, and that amplification of the β-hairpin has resulted in 8-26-stranded barrels. Using an integrated approach that combines sequence and structural analyses we find events in which non-amplification diversification also increases barrel strand number. Our network-based analysis reveals strand-number evolutionary pathways, including one that progresses from a primordial 8-stranded barrel to 16-strands and further, to 18-strands. Among these are mechanisms of strand number accretion without domain duplication, like a loop-to-hairpin transition. These mechanisms illustrate perpetuation of repeat protein topology without genetic duplication, likely induced by the hydrophobic membrane. Finally, we find that the evolutionary trace is particularly prominent in the C-terminal half of OMPs, implicating this region in the nucleation of OMP folding.

Article and author information

Author details

  1. Meghan Whitney Franklin

    Center for Computational Biology, University of Kansas, Lawrence, United States
    Competing interests
    No competing interests declared.
  2. Sergey Nepomnyachyi

    Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
    Competing interests
    No competing interests declared.
  3. Ryan Feehan

    Center for Computational Biology, University of Kansas, Lawrence, United States
    Competing interests
    No competing interests declared.
  4. Nir Ben-Tal

    Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
    Competing interests
    Nir Ben-Tal, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6901-832X
  5. Rachel Kolodny

    Department of Computer Science, University of Haifa, Haifa, Israel
    Competing interests
    No competing interests declared.
  6. Joanna SG Slusky

    Center for Computational Biology, University of Kansas, Lawrence, United States
    For correspondence
    slusky@ku.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0842-6340

Funding

National Institute of General Medical Sciences (DP2GM128201)

  • Meghan Whitney Franklin
  • Joanna SG Slusky

Gordon and Betty Moore Foundation (Moore Inventor Fellowship)

  • Joanna SG Slusky

National Science Foundation (MCB160205)

  • Joanna SG Slusky

Israel Science Foundation (450/16)

  • Nir Ben-Tal
  • Rachel Kolodny

National Institute of General Medical Sciences (P20GM103418)

  • Meghan Whitney Franklin
  • Joanna SG Slusky

National Institute of General Medical Sciences (P20GM113117)

  • Meghan Whitney Franklin
  • Joanna SG Slusky

National Institute of General Medical Sciences (T32-GM008359)

  • Meghan Whitney Franklin
  • Joanna SG Slusky

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

Reviewing Editor

  1. Vikas Nanda, Rutgers University, United States

Publication history

  1. Received: July 20, 2018
  2. Accepted: November 28, 2018
  3. Accepted Manuscript published: November 29, 2018 (version 1)

Copyright

© 2018, Franklin 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.

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