ArticleProtein science : a publication of the Protein Society2022
BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.
Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- NbBayesLM: bayesian prediction of nanobody thermostability using protein language model.Frontiers in bioinformatics · 2026Article
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025Article
- Article
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- Article
- Structure-based self-supervised learning enables ultrafast protein stability prediction upon mutation.Innovation (Cambridge (Mass.)) · 2025Article
- Engineering of a hydroxysteroid dehydrogenase with simultaneous enhancement in activity and thermostability for efficient biosynthesis of ursodeoxycholic acid.Applied and environmental microbiology · 2024Article
- Transfer learning to leverage larger datasets for improved prediction of protein stability changes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.Protein science : a publication of the Protein Society · 2024Article
- High-Temperature Tolerance Protein Engineering through Deep Evolution.Biodesign research · 2024Article
- Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry.RSC advances · 2023Review
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- PROSTATA: a framework for protein stability assessment using transformers.Bioinformatics (Oxford, England) · 2023Article
- Transfer learning to leverage larger datasets for improved prediction of protein stability changes.bioRxiv : the preprint server for biology · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Protein science : a publication of the Protein Society · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimating Gibbs free energy change of the protein remained a challenge. Some methods struggle to generalize on examples with no homology and produce uncalibrated predictions. Here we leverage advances in graph neural networks for protein feature extraction to tackle this structure-property prediction task. Our method, BayeStab, is then tested on four test datasets, including S669, S611, S350, and Myoglobin, showing high generalization and symmetry performance. Meanwhile, we apply concrete dropout enabled Bayesian neural networks to infer plausible models and estimate uncertainty. By decomposing the uncertainty into parts induced by data noise and model, we demonstrate that the probabilistic method allows insights into the inherent noise of the training datasets, which is closely relevant to the upper bound of the task. Finally, the BayeStab web server is created and can be found at: http://www.bayestab.com. The code for this work is available at: https://github.com/HongzhouTang/BayeStab.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.