ArticleeLife2023
Rapid protein stability prediction using deep learning representations.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed, 161 citations in OpenAlex.
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- Proteomic investigation of signaling dynamics: from static maps to network rewiring.Bioscience reports · 2026Review
- Deciphering protein mutation-phenotype linkages from CRISPR-based tiling mutagenesis screens.Cell systems · 2026Article
- Growth factor thermostability dataset derived from experimental and computational approaches to accelerate cultured meat development.Data in brief · 2026Article
- Interpreting the effects of DNA polymerase variants at the structural level.Molecular oncology · 2026Article
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- OrgNet+: towards robust protein stability prediction with convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- Probe-based identification of metal-binding sites using deep learning representations.Nature communications · 2026Article
- A functional map of phosphoprotein phosphatase regulation identifies an evolutionarily conserved reductase for the catalytic metal ions.Nature communications · 2026Article
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- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- Adaptive loss of function accelerated the evolution of ancient and modern human cognition.bioRxiv : the preprint server for biology · 2026Article
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
- DEX: a consensus-based amino acid exchangeability measure for improved codon substitution modelling.bioRxiv : the preprint server for biology · 2026Article
- Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.BMC microbiology · 2026Article
- Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI.bioRxiv : the preprint server for biology · 2026Article
29 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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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.