ArticleBMC bioinformatics2023
Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 25 citations in OpenAlex.
- Investigating the effects of missense mutations on the dual Ras/Rap activation mechanism of SYNGAP1 using molecular modelling.Journal of structural biology: X · 2026Article
- Computational protein stability analysis of SCN1A missense variants reveals domain-dependent stability patterns.Epilepsia open · 2026Article
- AI-GuidedJournal of the American Chemical Society · 2026Article
- In Silico Saturation-Mutagenesis-Based Genomic Mutation Risk Assessment for Enterovirus B.Viruses · 2026Article
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- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- Engineering of genetically encoded programmable calcium channel inhibitory binders.Nature communications · 2026Article
- Multiscale Computational Dissection of CCRL2-Mediated Chemerin Presentation.Journal of chemical information and modeling · 2025Article
- ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-guided rational engineering of synergistic PD-1 and LAG-3 blockade for enhanced tumor immunomodulation.Journal of computer-aided molecular design · 2025Article
- Computational Evidence for Digenic Contribution ofInternational journal of molecular sciences · 2025Article
- Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.Journal of chemical information and modeling · 2025Article
- FoldX force field revisited, an improved version.Bioinformatics (Oxford, England) · 2025Article
- Systematic use of protein free energy changes for classifying variants of uncertain significance: the case of IFT140 in Mainzer-Saldino Syndrome.Frontiers in molecular biosciences · 2025Article
- A Novel Missense Variant ofHuman mutation · 2025Article
- Advances in the Directed Evolution of Computer-aided Enzymes.Current topics in medicinal chemistry · 2025Review
- Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data.Scientific reports · 2024Article
- Understanding large scale sequencing datasets through changes to protein folding.Briefings in functional genomics · 2024Review
- Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1.Briefings in bioinformatics · 2024Article
- Insights into Transient Dimerisation of Carnitine/Acylcarnitine Carrier (SLC25A20) from Sarkosyl/PAGE, Cross-Linking Reagents, and Comparative Modelling Analysis.Biomolecules · 2024Article
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4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundComputational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability.
resultsWe show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation.
conclusionsUsing a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.
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