ArticleInternational journal of molecular sciences2026
Multi-State Structural Genomics Enables Large-Scale, Mechanistic, and Context-Specific Classification of ABCC6 Genetic Variants Implicated in Calcification Diseases.
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- ARID1B damaging variants from more than one million genomes, cause human diseases by impairing protein-protein interactions, stability, and regulation.bioRxiv : the preprint server for biology · 2026Article
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Abstract
Genetic variation in ATP Binding Cassette Subfamily C Member 6 (ABCC6) can cause both pseudoxanthoma elasticum (PXE) and generalized arterial calcification of infancy (GACI). There are 930 distinct missense variants in ABCC6 reported, 87% of which are of uncertain clinical significance (VUS). New approaches are needed to mechanistically interpret and classify these VUS. We developed 3D protein models of ABCC6 in three functionally relevant conformations to calculate the structural effects of variants. We also used three-dimensional (3D) hotspot detection and developed a mechanistic ontology for critical structure-based functions of ABCC6, enabling us to categorize genomic variants. We identified two 3D hotspots and six specific functions of ABCC6 which variants impact. From this, we propose a mechanism for pathogenicity for 41% of VUS according to their impacted function. We propose that 33 of these variants could be reclassified as Likely Pathogenic with the addition of these structure-based evidence. The mechanistic information we present will guide future research to better address calcification disorders and understand genetic variants. This work emphasizes the benefits of thorough, holistic, and protein-based approaches to genetic interpretation. Further, our VUS reclassification approach will improve the diagnosis of ABCC6-driven diseases, shortening diagnostic odysseys. We believe that computational structural genomics approaches will soon take prominence in genomics data interpretation and variant classification.
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