ArticleBMC bioinformatics2010
Predicting changes in protein thermostability brought about by single- or multi-site mutations.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- FireProt 2.0: web-based platform for the fully automated design of thermostable proteins.Briefings in bioinformatics · 2023Article
- Machine Learning for Protein Engineering.ArXiv · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- Discrepancies of RET gene and risk of differentiated thyroid carcinoma.Cancer biomarkers : section A of Disease markers · 2022Article
- Cluster learning-assisted directed evolution.Nature computational science · 2021Article
- Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.Computational and structural biotechnology journal · 2020Review
- Machine-learning-guided directed evolution for protein engineering.Nature methods · 2019Review
- Compensated pathogenic variants in coagulation factors VIII and IX present complex mapping between molecular impact and hemophilia severity.Scientific reports · 2019Article
- Sequence Tolerance of a Single-Domain Antibody with a High Thermal Stability: Comparison of Computational and Experimental Fitness Profiles.ACS omega · 2019Article
- The Site-Specific Amino Acid Preferences of Homologous Proteins Depend on Sequence Divergence.Genome biology and evolution · 2019Article
- mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion.Bioinformatics (Oxford, England) · 2018Article
- Amino-Acid Network Clique Analysis of Protein Mutation Non-Additive Effects: A Case Study of Lysozme.International journal of molecular sciences · 2018Article
- Enhanced Thermostability of Glucose Oxidase through Computer-Aided Molecular Design.International journal of molecular sciences · 2018Article
- FoldX as Protein Engineering Tool: Better Than Random Based Approaches?Computational and structural biotechnology journal · 2018Review
- Stabilization of a nucleotide-binding domain of the cystic fibrosis transmembrane conductance regulator yields insight into disease-causing mutations.The Journal of biological chemistry · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (DeltaDeltaG) of thermal denaturation.
resultsWe have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations.
conclusionsThe performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.
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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.