ArticleProteins2016
Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding.
Article in Proteins, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
32 citing papers in PubMed.
- A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations.Cell reports methods · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.Human genomics · 2024Article
- Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy.International journal of molecular sciences · 2024Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
- On the linkage of thermodynamics and pathogenicity.Current opinion in structural biology · 2023Review
- Electrostatics in Computational Biophysics and Its Implications for Disease Effects.International journal of molecular sciences · 2022Review
- Implications of disease-related mutations at protein-protein interfaces.Current opinion in structural biology · 2022Review
- LYRUS: a machine learning model for predicting the pathogenicity of missense variants.Bioinformatics advances · 2022Article
- Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants.American journal of human genetics · 2021Article
- The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome.Journal of molecular biology · 2021Article
- SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability.International journal of molecular sciences · 2021Article
- PremPS: Predicting the impact of missense mutations on protein stability.PLoS computational biology · 2020Article
- Protein-protein and protein-nucleic acid binding residues important for common and rare sequence variants in human.BMC bioinformatics · 2020Article
- Article
- Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2Biomolecules · 2020Review
- Mutation severity spectrum of rare alleles in the human genome is predictive of disease type.PLoS computational biology · 2020Article
- SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function.Bioinformatics (Oxford, England) · 2020Article
- SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.International journal of molecular sciences · 2020Article
- Analysis of protein missense alterations by combining sequence- and structure-based methods.Molecular genetics & genomic medicine · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions was assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here it is demonstrated that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease-causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, correlation coefficients (CC) between the disease index (Pd) and relative folding (Ppr,f) and binding (Ppr,b) probability indexes, respectively, was achieved. The obtained CCs demonstrated the applicability of the proposed approach and it served as good indicator for SAV association with disease.
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