Evidence map›Paper›PMID 36173174›Full record

ArticleProtein science : a publication of the Protein Society2022

Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations.

David F Thieker, Jack B Maguire, Stephan T Kudlacek, Andrew Leaver-Fay, Sergey Lyskov, Brian Kuhlman

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. AFrontiers in immunology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Enhanced l-serine synthesis inSynthetic and systems biotechnology · 2025
    Article
  9. Article
  10. Article
  11. Review
  12. Protein stability models fail to capture epistatic interactions of double point mutations.Protein science : a publication of the Protein Society · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. High-throughput system for the thermostability analysis of proteins.Protein science : a publication of the Protein Society · 2024
    Article
  17. Transfer learning to leverage larger datasets for improved prediction of protein stability changes.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  18. Article
  19. Genes · 2023
    Article
  20. Biomolecules · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

David F ThiekerDepartment of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Jack B MaguireDepartment of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Stephan T KudlacekDepartment of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Andrew Leaver-FayDepartment of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Sergey LyskovDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Brian KuhlmanDepartment of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.ORCID 0000-0003-4907-9699

Funding

GPU workstation for deep learning-based protein design and cryo-EM data processingR35GM131923 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BRIAN A KUHLMAN · 2019 to 2026
$6.1M
NIGMS NIH HHS R35 GM131923NIGMS NIH HHS R35GM131923
6 · The paper itself

Abstract

Many proteins have low thermodynamic stability, which can lead to low expression yields and limit functionality in research, industrial and clinical settings. This article introduces two, web-based tools that use the high-resolution structure of a protein along with the Rosetta molecular modeling program to predict stabilizing mutations. The protocols were recently applied to three genetically and structurally distinct proteins and successfully predicted mutations that improved thermal stability and/or protein yield. In all three cases, combining the stabilizing mutations raised the protein unfolding temperatures by more than 20°C. The first protocol evaluates point mutations and can generate a site saturation mutagenesis heatmap. The second identifies mutation clusters around user-defined positions. Both applications only require a protein structure and are particularly valuable when a deep multiple sequence alignment is not available. These tools were created to simplify protein engineering and enable research that would otherwise be infeasible due to poor expression and stability of the native molecule.

Indexed as

Protein EngineeringProteinsModels, MolecularMutationThermodynamicsProteinsmolecular modelingprotein designprotein engineeringprotein stabilityRosetta molecular modeling program

Identifiers

PMID36173174
PMCPMC9490798

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.