Precise assembly of complex beta sheet topologies from de novo designed building blocks

  1. Indigo Chris King  Is a corresponding author
  2. James Gleixner
  3. Lindsey Doyle
  4. Alexandre Kuzin
  5. John F Hunt
  6. Rong Xiao
  7. Gaetano T Montelione
  8. Barry L Stoddard
  9. Frank DiMaio
  10. David Baker
  1. University of Washington, United States
  2. Fred Hutchinson Cancer Research Center, United States
  3. Columbia University, United States
  4. Rutgers, The State University of New Jersey, United States

Abstract

Design of complex alpha-beta protein topologies poses a challenge because of the large number of alternative packing arrangements. A similar challenge presumably limited the emergence of large and complex protein topologies in evolution. Here we demonstrate that protein topologies with six and seven-stranded beta sheets can be designed by insertion of one de novo designed beta sheet containing protein into another such that the two beta sheets are merged to form a single extended sheet, followed by amino acid sequence optimization at the newly formed strand-strand, strand-helix, and helix-helix interfaces. Crystal structures of two such designs closely match the computational design models. Searches for similar structures in the SCOP protein domain database yield only weak matches with different beta sheet connectivities. A similar beta sheet fusion mechanism may have contributed to the emergence of complex beta sheets during natural protein evolution.

Article and author information

Author details

  1. Indigo Chris King

    Molecular Engineering and Sciences Building, University of Washington, Seattle, United States
    For correspondence
    chrisk1@uw.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. James Gleixner

    Institute for Protein Design, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Lindsey Doyle

    Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Alexandre Kuzin

    Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. John F Hunt

    Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Rong Xiao

    Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Gaetano T Montelione

    Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Northeast Struc-tural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Barry L Stoddard

    Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Frank DiMaio

    Institute for Protein Design, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. David Baker

    Institute for Protein Design, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.

Copyright

© 2015, King 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

  • 2,266
    views
  • 517
    downloads
  • 14
    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. Indigo Chris King
  2. James Gleixner
  3. Lindsey Doyle
  4. Alexandre Kuzin
  5. John F Hunt
  6. Rong Xiao
  7. Gaetano T Montelione
  8. Barry L Stoddard
  9. Frank DiMaio
  10. David Baker
(2015)
Precise assembly of complex beta sheet topologies from de novo designed building blocks
eLife 4:e11012.
https://doi.org/10.7554/eLife.11012

Share this article

https://doi.org/10.7554/eLife.11012

Further reading

    1. Biochemistry and Chemical Biology
    2. Structural Biology and Molecular Biophysics
    Joar Esteban Pinto Torres, Mathieu Claes ... Yann G-J Sterckx
    Research Article

    African trypanosomes are the causative agents of neglected tropical diseases affecting both humans and livestock. Disease control is highly challenging due to an increasing number of drug treatment failures. African trypanosomes are extracellular, blood-borne parasites that mainly rely on glycolysis for their energy metabolism within the mammalian host. Trypanosomal glycolytic enzymes are therefore of interest for the development of trypanocidal drugs. Here, we report the serendipitous discovery of a camelid single-domain antibody (sdAb aka Nanobody) that selectively inhibits the enzymatic activity of trypanosomatid (but not host) pyruvate kinases through an allosteric mechanism. By combining enzyme kinetics, biophysics, structural biology, and transgenic parasite survival assays, we provide a proof-of-principle that the sdAb-mediated enzyme inhibition negatively impacts parasite fitness and growth.

    1. Structural Biology and Molecular Biophysics
    Manming Xu, Sarath Chandra Dantu ... Shozeb Haider
    Research Article

    The relationship between protein dynamics and function is essential for understanding biological processes and developing effective therapeutics. Functional sites within proteins are critical for activities such as substrate binding, catalysis, and structural changes. Existing computational methods for the predictions of functional residues are trained on sequence, structural, and experimental data, but they do not explicitly model the influence of evolution on protein dynamics. This overlooked contribution is essential as it is known that evolution can fine-tune protein dynamics through compensatory mutations either to improve the proteins’ performance or diversify its function while maintaining the same structural scaffold. To model this critical contribution, we introduce DyNoPy, a computational method that combines residue coevolution analysis with molecular dynamics simulations, revealing hidden correlations between functional sites. DyNoPy constructs a graph model of residue–residue interactions, identifies communities of key residue groups, and annotates critical sites based on their roles. By leveraging the concept of coevolved dynamical couplings—residue pairs with critical dynamical interactions that have been preserved during evolution—DyNoPy offers a powerful method for predicting and analysing protein evolution and dynamics. We demonstrate the effectiveness of DyNoPy on SHV-1 and PDC-3, chromosomally encoded β-lactamases linked to antibiotic resistance, highlighting its potential to inform drug design and address pressing healthcare challenges.