ReviewBriefings in bioinformatics2020
A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.
Review in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Computational rational design of unspecific peroxygenase for C-H oxidation.Science advances · 2026Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- Accurate predictions of protein mutational effects accelerated with a hybrid-topology free energy protocol.Communications chemistry · 2025Article
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Replication of MissenseGenes · 2025Article
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- ProstaNet: A Novel Geometric Vector Perceptrons-Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation.Research (Washington, D.C.) · 2025Article
- A systematic in-silico functional and structural analysis reveals deleterious missense nsSNPs in the humanMolecular biology research communications · 2025Article
- Article
- Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method.BMC bioinformatics · 2024Article
- Deep mutational scanning reveals sequence to function constraints for SWEET family transporters.bioRxiv : the preprint server for biology · 2024Article
- Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models.Bioinformatics (Oxford, England) · 2024Article
- Predicting Mutational Effects on CaJournal of the American Chemical Society · 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
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- PROSTATA: a framework for protein stability assessment using transformers.Bioinformatics (Oxford, England) · 2023Article
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
- 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
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Abstract
A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.
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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.