ArticleBriefings in bioinformatics2021
On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models.Bioinformatics (Oxford, England) · 2024Article
- PROSTATA: a framework for protein stability assessment using transformers.Bioinformatics (Oxford, England) · 2023Article
- The role of data imbalance bias in the prediction of protein stability change upon mutation.PloS one · 2023Article
- Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset.Briefings in bioinformatics · 2022Article
- Challenges in predicting stabilizing variations: An exploration.Frontiers in molecular biosciences · 2022Article
- A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations.Genes · 2021Article
- PremPS: Predicting the impact of missense mutations on protein stability.PLoS computational biology · 2020Article
- Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks.PLoS computational biology · 2020Article
- Identification of pathogenic missense mutations using protein stability predictors.Scientific reports · 2020Article
- Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.Computational and structural biotechnology journal · 2020Review
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4 authors.
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Abstract
A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A- > B) and its reverse (B- > A) must have the opposite value of the free energy difference (ΔΔGAB = - ΔΔGBA). In this letter, we complement the Fang's paper presenting a more general view of the problem. In particular, a machine learning-based method, published in 2015 (INPS), addressed the bias issue directly. We include the analysis of the missing method, showing that INPS is nearly insensitive to the addressed problem.
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