ArticleScientific reports2018
Prediction and interpretation of deleterious coding variants in terms of protein structural stability.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Integrated Structural and Dynamic Analysis Reveals Destabilizing Effects of KRAS Missense Variants Associated with Lung Cancer.The protein journal · 2026Article
- In silico functional, structural, and pathogenicity assessment of single nucleotide polymorphisms in the human SOX9 gene.Scientific reports · 2025Article
- SOuLMuSiC, a novel tool for predicting the impact of mutations on protein solubility.Scientific reports · 2025Article
- Residue conservation and solvent accessibility are (almost) all you need for predicting mutational effects in proteins.Bioinformatics (Oxford, England) · 2025Article
- Genetic diversity and natural selection of Plasmodium falciparum Pf41 vaccine candidate in clinical isolates from Senegal.Scientific reports · 2025Article
- Critical assessment of missense variant effect predictors on disease-relevant variant data.Human genetics · 2025Article
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.Human genetics · 2025Article
- Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.Human genetics · 2025Article
- AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.Protein science : a publication of the Protein Society · 2025Article
- A methodology for calculating the rarity of diverse proteins based on functional specificity and thermodynamic stability.PloS one · 2025Article
- Structural and functional prediction, evaluation, and validation in the post-sequencing era.Computational and structural biotechnology journal · 2024Review
- Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method.BMC bioinformatics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.bioRxiv : the preprint server for biology · 2024Article
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene.Scientific reports · 2024Article
- FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction.Human genomics · 2024Article
- Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.Genes · 2023Article
- A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.Briefings in bioinformatics · 2023Review
- Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/ .
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