Abstract

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.

Data availability

Scripts and data to repeat our analyses are available via: https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg/

The following data sets were generated

Article and author information

Author details

  1. Lasse M Blaabjerg

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  2. Maher M Kassem

    Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  3. Lydia L Good

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5308-8542
  4. Nicolas Jonsson

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7838-1814
  5. Matteo Cagiada

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  6. Kristoffer E Johansson

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  7. Wouter Boomsma

    Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
    For correspondence
    wb@di.ku.dk
    Competing interests
    The authors declare that no competing interests exist.
  8. Amelie Stein

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    For correspondence
    amelie.stein@bio.ku.dk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5862-1681
  9. Kresten Lindorff-Larsen

    Department of Biology, University of Copenhagen, Copenhagen, Denmark
    For correspondence
    lindorff@bio.ku.dk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4750-6039

Funding

Novo Nordisk Fonden (NNF18OC0033950)

  • Amelie Stein
  • Kresten Lindorff-Larsen

Novo Nordisk Fonden (NNF20OC0062606)

  • Wouter Boomsma

Novo Nordisk Fonden (NNF18OC0052719)

  • Wouter Boomsma

Lundbeckfonden (R272-2017-4528)

  • Amelie Stein

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

Reviewing Editor

  1. José D Faraldo-Gómez, National Heart, Lung and Blood Institute, United States

Publication history

  1. Received: August 10, 2022
  2. Accepted: May 12, 2023
  3. Accepted Manuscript published: May 15, 2023 (version 1)

Copyright

© 2023, Blaabjerg 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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  1. Lasse M Blaabjerg
  2. Maher M Kassem
  3. Lydia L Good
  4. Nicolas Jonsson
  5. Matteo Cagiada
  6. Kristoffer E Johansson
  7. Wouter Boomsma
  8. Amelie Stein
  9. Kresten Lindorff-Larsen
(2023)
Rapid protein stability prediction using deep learning representations
eLife 12:e82593.
https://doi.org/10.7554/eLife.82593

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