ArticlePLoS genetics2017
Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
54 citing papers in PubMed, 117 citations in OpenAlex.
- Article
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- Interactions between rare variants in DNA repair genes and cardiometabolic risk explain more variability in cognitive function.GeroScience · 2026Article
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- Proteasomal control of transcription factors: mechanisms, regulation and dysregulation.Cellular and molecular life sciences : CMLS · 2026Review
- Functional assessment of protein variants in structured domains by fluorescence cross-correlation spectroscopy.Scientific reports · 2026Article
- ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence.American journal of human genetics · 2025Article
- Shared-weight graph framework for comprehensive protein stability prediction across diverse mutation types.Briefings in bioinformatics · 2025Article
- When "loss-of-function" means proteostasis burden: Thinking again about coding DNA variants.American journal of human genetics · 2025Article
- VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance.Computational and structural biotechnology journal · 2025Article
- SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions.Nature communications · 2024Article
- Deep mutational scanning reveals a correlation between degradation and toxicity of thousands of aspartoacylase variants.Nature communications · 2024Article
- Characterizing glucokinase variant mechanisms using a multiplexed abundance assay.Genome biology · 2024Article
- A mutational atlas for Parkin proteostasis.Nature communications · 2024Article
- Exploring the "misfolding problem" by systematic discovery and analysis of functional-but-degraded proteins.Molecular biology of the cell · 2023Article
- Lynch syndrome-associated endometrial cancer patient with a rare novel germline likely pathogenic variant of MSH2 gene: A case report.Gynecologic oncology reports · 2023Article
- Interpreting the molecular mechanisms of disease variants in human transmembrane proteins.Biophysical journal · 2023Article
- Analysis of genome instability and implications for the consequent phenotype inMicrobial genomics · 2023Article
- Lynch syndrome, molecular mechanisms and variant classification.British journal of cancer · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases.
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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.