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Rapid protein stability prediction using deep learning representations
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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 ∼ 230 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.
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- Linderstrøm-Lang Centre for Protein Science, University of Copenhagen. 2023. Rapid protein stability prediction using deep learning representations. Software Heritage. swh:1:rev:fb9438fa4724171e752fdf70e270db0cffc0f73c. https://archive.softwareheritage.org/swh:1:dir:3b5a5bf941013f41b9f7333e4190f4c9c4924cba;origin=https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg;visit=swh:1:snp:2763a4dfde8edc3ee726a2aeb5897a90fc421c7c;anchor=swh:1:rev:fb9438fa4724171e752fdf70e270db0cffc0f73c.
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Metadata
- Authors
- Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., & Lindorff-Larsen, K.
- Affiliations
- University of Copenhagen (DK)
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen
- Corresponding author
- Boomsma, Wouter
- Stein, Amelie
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2023-05-15
- Volume
- 12
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC10266766
- OpenAlex
- W4376612841
- Cited by
- 177
- References
- 66
- Citations by year
- 2026: 48 · 2025: 59 · 2024: 48 · 2023: 16
- FWCI
- 15.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Novo Nordisk Fonden (NNF18OC0033950, NNF20OC0062606, NNF18OC0052719)
- Lundbeckfonden (R272-2017-4528)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- protein stability prediction, thermodynamic stability, RASP, saturation mutagenesis, single amino acid variants, pathogenic variants, genetic disorders, human proteome, protein engineering, protein evolution
- MeSH terms
- Deep Learning, Amino Acids, Humans, Proteins, Mutagenesis, Computational Biology, Protein Stability
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