ArticleBMC genomics2019
ProTstab - predictor for cellular protein stability.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Dynamics-informed multigraph neural networks for protein thermostability prediction and residue-level interpretation.iScience · 2026Article
- 3D Proteomics: Structural, Functional, Chemical and Biomarker Discovery Proteomics With LiP-MS.Molecular & cellular proteomics : MCP · 2026Review
- FireProtDB 2.0: large-scale manually curated database of the protein stability data.Nucleic acids research · 2026Article
- Decoding protein dynamics with limited proteolysis coupled to mass spectrometry: A comprehensive review.Journal of pharmaceutical analysis · 2025Review
- Deciphering the determinants of recombinant protein expression across the human secretome.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Enzyme miniaturization: Revolutionizing future biocatalysts.Biotechnology advances · 2025Review
- Prediction and design of thermostable proteins with a desired melting temperature.Scientific reports · 2025Article
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms.Bioinformatics advances · 2024Article
- Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.Genes · 2023Article
- PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate.International journal of molecular sciences · 2023Article
- Predicting thermostability difference between cellular protein orthologs.Bioinformatics (Oxford, England) · 2023Article
- DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability.International journal of molecular sciences · 2023Article
- Evaluation of CRISPR/Cas9 exon-skipping vector for choroideremia using human induced pluripotent stem cell-derived RPE.The journal of gene medicine · 2023Article
- DeepTP: A Deep Learning Model for Thermophilic Protein Prediction.International journal of molecular sciences · 2023Article
- BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Protein science : a publication of the Protein Society · 2022Article
- ProTstab2 for Prediction of Protein Thermal Stabilities.International journal of molecular sciences · 2022Article
- Lymphocytes Utilize Somatic Mutations, Epigenetic Silencing, and the Proteasome to Escape Truncated WASP Expression.Journal of clinical immunology · 2022Article
- Hidden in plain sight: challenges in proteomics detection of small ORF-encoded polypeptides.microLife · 2022Article
- Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundStability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples.
resultsWe took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities.
conclusionsThe Pearson's correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.
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Registered trials
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