Dynamicasome: a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918147136684032 |
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| author | Islam, Naeyma N Coban, Mathew A Fuller, Jessica M Weber, Caleb Chitale, Rohit Jussila, Benjamin Brock, Trisha J. Tao, Cui Caulfield, Thomas R |
| author_facet | Islam, Naeyma N Coban, Mathew A Fuller, Jessica M Weber, Caleb Chitale, Rohit Jussila, Benjamin Brock, Trisha J. Tao, Cui Caulfield, Thomas R |
| contents | Advances in genomic medicine accelerate the identi cation 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 signi cance. We believe this model helps alleviate the burden of unknown variants in genomic medicine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamicasome: a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations Islam, Naeyma N Coban, Mathew A Fuller, Jessica M Weber, Caleb Chitale, Rohit Jussila, Benjamin Brock, Trisha J. Tao, Cui Caulfield, Thomas R Quantitative Methods Artificial Intelligence Biological Physics Molecular Networks Advances in genomic medicine accelerate the identi cation 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 signi cance. We believe this model helps alleviate the burden of unknown variants in genomic medicine. |
| title | Dynamicasome: a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations |
| topic | Quantitative Methods Artificial Intelligence Biological Physics Molecular Networks |
| url | https://arxiv.org/abs/2509.19766 |