Evidence map›Paper›PMID 37283042›Full record

ArticleNucleic acids research2023

DDMut: predicting effects of mutations on protein stability using deep learning.

Yunzhuo Zhou, Qisheng Pan, Douglas E V Pires, Carlos H M Rodrigues, David B Ascher

Abstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers.

0numbers the graph read from it
0cells of the map it votes in
143citing 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

143 citing papers in PubMed.

  1. Article
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  4. A dominant mutation in tomato DNA POLYMERASE DELTA 1 causes geminivirus DNA replication catastrophe.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  9. A novel IDS variant associated with an isolated ocular phenotype in Hunter syndrome.Documenta ophthalmologica. Advances in ophthalmology · 2026
    Article
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  15. Review
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83 more citing papers are in PubMed but not listed here.

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

5 authors.

Yunzhuo ZhouSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia.
Qisheng PanSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia.
Douglas E V PiresSchool of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0002-3004-2119
Carlos H M RodriguesSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia.
David B AscherSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia.ORCID 0000-0003-2948-2413

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongoing limitations, including computational time, low predictive power, and biased predictions towards destabilising mutations. To fill this gap, we developed DDMut, a fast and accurate siamese network to predict changes in Gibbs Free Energy upon single and multiple point mutations, leveraging both forward and hypothetical reverse mutations to account for model anti-symmetry. Deep learning models were built by integrating graph-based representations of the localised 3D environment, with convolutional layers and transformer encoders. This combination better captured the distance patterns between atoms by extracting both short-range and long-range interactions. DDMut achieved Pearson's correlations of up to 0.70 (RMSE: 1.37 kcal/mol) on single point mutations, and 0.70 (RMSE: 1.84 kcal/mol) on double/triple mutants, outperforming most available methods across non-redundant blind test sets. Importantly, DDMut was highly scalable and demonstrated anti-symmetric performance on both destabilising and stabilising mutations. We believe DDMut will be a useful platform to better understand the functional consequences of mutations, and guide rational protein engineering. DDMut is freely available as a web server and API at https://biosig.lab.uq.edu.au/ddmut.

Indexed as

Deep LearningProteinsProtein StabilitySoftwareMutationPoint MutationProteins

Identifiers

PMID37283042
PMCPMC10320186

What OpenQuestion holds

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LicenceCC BY
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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.