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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HyperMPNN – A general strategy to design thermostable proteins learned from hyperthermophilesPreprint
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Review of predicting protein stability changes upon variations
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Computation of Protein Thermostability and Epistasis
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Structure‐guided engineering of protein stability through core hydrophobicity
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Stability Oracle: A Structure-Based Graph-Transformer for Identifying Stabilizing MutationsPreprint
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Computational studies on the functional and structural impact of pathogenic mutations in enzymes
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AI4Protein: transforming the future of protein design
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Predicting Saturation Concentrations of Phase-Separating Proteins via Thermodynamic Integration
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When “loss-of-function” means proteostasis burden: Thinking again about coding DNA variants
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AI-enabled alkaline-resistant evolution of protein to apply in mass production
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Decoding molecular mechanisms for loss-of-function variants in the human proteomePreprint
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HARPS: A Hybrid Algorithm for Robust Plant Stress Detection to Foster Sustainable Agriculture
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A computational pipeline for predicting distal hotspots in an artificial enzyme
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Complex state transitions of the bacterial cell division protein FtsZ
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Exploring Evolution to Uncover Insights Into Protein Mutational Stability
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Disentangling the mutational effects on protein stability and interaction of human MLH1
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Self-citationLearning the shape of protein micro-environments with a holographic convolutional neural networkPreprint
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MetaEnzyme: Meta Pan-Enzyme Learning for Task-Adaptive Redesign
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Improving Inverse Folding models at Protein Stability Prediction without additional Training or DataPreprint
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Predicting the structure-altering mechanisms of disease variants
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Supervised learning of protein variant effects across large‐scale mutagenesis datasets
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A structure-based in silico analysis of the Kell blood group system
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Mechanisms of inflammatory bowel diseases: Insights from human genetics and chemical biology
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Systematic characterization of indel variants using a yeast-based protein folding sensor
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Deep Local Analysis estimates effects of mutations on protein-protein interactionsPreprint
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HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability predictionPreprint
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Functionally important residues from graph analysis of coevolved dynamic couplings
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AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations
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Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation
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Mechanistic Mutational Scanning to Uncover the Secret Life of Proteins
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Rare Catechol- O -methyltransferase Missense Variants Are Structurally Unstable Proteasome Targets
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Destabilization and Degradation of a Disease-Linked PGM1 Protein Variant
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T-cell receptor specificity landscape revealed through de novo peptide designPreprint
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Effects of protein interface mutations on protein quality and affinityPreprint
de Kanter, J. K., Smorodina, E., Minnegalieva, A. R., Arts, M., Blaabjerg, L. M., Frolenkova, M., Rawat, P., Wolfram, L., Britze, H., Wilke, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.24.713863 · Cited by 2 · see citing works
Self-citation
- 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 · 2022: 6
- 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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