ArticleBriefings in bioinformatics2022
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- OrgNet+: towards robust protein stability prediction with convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Structural and Functional Variations of Enzymes Indicate Latitudinal Adaptations of Subarctic and Arctic Sea Urchins.Ecology and evolution · 2025Article
- DSSP 4: FAIR annotation of protein secondary structure.Protein science : a publication of the Protein Society · 2025Article
- Computational studies on the functional and structural impact of pathogenic mutations in enzymes.Protein science : a publication of the Protein Society · 2025Review
- Functionally constrained human proteins are less prone to mutational instability from single amino acid substitutions.Nature communications · 2025Article
- Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges.Human genetics · 2025Article
- An in silico framework to visualize how cancer-associated mutations influence structural plasticity of the chemokine receptor CCR3.Protein science : a publication of the Protein Society · 2025Article
- Energy metric prediction for double insertion mutants via the RoseNet deep learning framework.Bioinformatics advances · 2025Article
- Using AI-predicted protein structures as a reference to predict loss-of-function activity in tumor suppressor breast cancer genes.Computational and structural biotechnology journal · 2024Article
- PRITrans: A Transformer-Based Approach for the Prediction of the Effects of Missense Mutation on Protein-RNA Interactions.International journal of molecular sciences · 2024Article
- AlzDiscovery: A computational tool to identify Alzheimer's disease-causing missense mutations using protein structure information.Protein science : a publication of the Protein Society · 2024Article
- Characteristics of SARS-CoV-2 Omicron BA.5 variants in Shanghai after ending the zero-COVID policy in December 2022: a clinical and genomic analysis.Frontiers in microbiology · 2024Article
- Two novel CHN1 variants identified in Duane retraction syndrome pedigrees disrupt development of ocular motor nerves in zebrafish.Journal of human genetics · 2024Article
- Characterization on the oncogenic effect of the missense mutations of p53 via machine learning.Briefings in bioinformatics · 2023Article
- GTExome: Modeling commonly expressed missense mutations in the human genome.bioRxiv : the preprint server for biology · 2023Article
- Identifying the Molecular Drivers of Pathogenic Aldehyde Dehydrogenase Missense Mutations in Cancer and Non-Cancer Diseases.International journal of molecular sciences · 2023Article
- Accelerated Molecular Dynamics for Peptide Folding: Benchmarking Different Combinations of Force Fields and Explicit Solvent Models.Journal of chemical information and modeling · 2023Article
- AlphaFold2 and its applications in the fields of biology and medicine.Signal transduction and targeted therapy · 2023Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.
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