Dynamicasome—a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations

• Published in: Communications biology Vol. 8; no. 1; pp. 958 - 14
• Main Authors: Islam, Naeyma N., Coban, Mathew A., Fuller, Jessica M., Weber, Caleb, Chitale, Rohit, Jussila, Benjamin, Brock, Trisha J., Tao, Cui, Caulfield, Thomas R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2413; 631/535; 631/57/2266; 64/11; 692/420/2489; Accuracy; Algorithms; Artificial Intelligence; Biomedical and Life Sciences; Decision making; Genes; Genomics; Humans; Life Sciences; Mixed Function Oxygenases - chemistry; Mixed Function Oxygenases - genetics; Molecular Dynamics Simulation; Molecular modelling; Mutation; Neural networks; Neural Networks, Computer; Pathogenicity; Physiology; Proteins
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-025-08334-y

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. AI models integrating molecular dynamics data improve pathogenicity prediction in PMM2 mutations, which can outperform existing tools and helps in classifying variants of uncertain significance aiding genomic translational medicine.