ArticleBriefings in bioinformatics2022
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
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83 citing papers in PubMed.
- The landscape of gene loss and missense variation across the mammalian tree informs on gene essentiality.Nature genetics · 2026Article
- StrucNS reveals interaction-weighted network topology as the driving predictor of absolute stability of natural and de novo proteins.Bioinformatics (Oxford, England) · 2026Article
- Aligning protein-generative models to experimental fitness with ProteinDPO.Nature methods · 2026Article
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 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
- Improving high-degree-polymerization GOS synthesis of a marine cold-active β-galactosidase via semi-rational design.Archives of microbiology · 2026Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- DPLM: Dynamics-aware Protein Language Model via contrastive learning between sequence and molecular dynamics simulation trajectory.bioRxiv : the preprint server for biology · 2026Article
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Protein folding stability estimation with explicit consideration of unfolded states.Nature communications · 2026Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.bioRxiv : the preprint server for biology · 2026Article
- First LDLRAP1 and Recurrent LDLR Mutations in Tunisian Families With Familial Hypercholesterolemia.Journal of cellular and molecular medicine · 2026Article
- Leptin receptor rs1137101 polymorphism and altered leptin-sOb-R axis contribute to type 2 diabetes risk in Gujarat population.Frontiers in endocrinology · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
23 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and 'all' available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21-0.5 and 0-0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51-0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the $\Delta \Delta G$ predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before.
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