ArticleInternational journal of molecular sciences2018
PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- FireProtDB 2.0: large-scale manually curated database of the protein stability data.Nucleic acids research · 2026Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease via Advanced Machine Learning Among Chinese Han Population.Obesity surgery · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- PON-P3: Accurate Prediction of Pathogenicity of Amino Acid Substitutions.International journal of molecular sciences · 2025Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.ArXiv · 2024Article
- Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.Journal of molecular biology · 2023Article
- Article
- Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.Acta biochimica et biophysica Sinica · 2023Review
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- Prediction of protein stability changes upon single-point variant using 3D structure profile.Computational and structural biotechnology journal · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- The role of data imbalance bias in the prediction of protein stability change upon mutation.PloS one · 2023Article
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
- PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms.Frontiers in molecular biosciences · 2022Article
- Protposer: The web server that readily proposes protein stabilizing mutations with high PPV.Computational and structural biotechnology journal · 2022Article
- PON-Sol2: Prediction of Effects of Variants on Protein Solubility.International journal of molecular sciences · 2021Article
- Performance of Web tools for predicting changes in protein stability caused by mutations.BMC bioinformatics · 2021Article
- ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years.Nucleic acids research · 2021Article
- ThermoMutDB: a thermodynamic database for missense mutations.Nucleic acids research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability.
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.