ArticleCommunications biology2025
Dynamicasome-a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- LC-MS/MS-based peptide identification, molecular simulations, and biological validation for targeted discovery of antioxidant peptides from large yellow croaker.Food chemistry: X · 2026Article
- Loss ofbioRxiv : the preprint server for biology · 2026Article
- Harnessing Nonlinear Mechanics to Transform Medical Diagnostics.Applied mechanics reviews · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Advances in genomic medicine accelerate the identification of mutations in disease-associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in diagnostics and clinical decision-making. Predictive AI models are generated to combat this issue, but current tools display low accuracy when tested against functionally validated datasets. We show that integrating detailed conformational data extracted from molecular dynamics simulations (MDS) into advanced AI-based models increases their predictive power. We carry out an exhaustive mutational analysis of the disease gene PMM2 and subject structural models of each variant to MDS. AI models trained on this dataset outperform existing tools when predicting the known pathogenicity of mutations. Our best performing model, a neuronal networks model, also predicts the pathogenicity of several PMM2 mutations currently considered of unknown significance. We believe this model helps alleviate the burden of unknown variants in genomic medicine.
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Registered trials
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