Dynamicasome: a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations
Naeyma N Islam, Mathew A Coban, Jessica M Fuller, Caleb Weber, Rohit Chitale, Benjamin Jussila, Trisha J. Brock, Cui Tao, Thomas R Caulfield
基因组医学的进步加速了疾病相关基因突变的识别阳离子,但许多突变的致病性仍然未知,阻碍了它们在诊断和临床决策中的使用。 生成预测性AI模型是为了解决这个问题,但目前的工具在针对功能验证的数据集进行测试时显示的精度较低。 我们表明,将分子动力学模拟(MDS)中提取的详细构象数据集成到基于AI的高级模型中会增加其预测能力。 我们对疾病基因PMM2和MDS每个变体的主题结构模型进行了详尽的突变分析。 在这个数据集上训练的AI模型在预测突变的已知致病性时优于现有工具。 我们表现最好的模型,神经元网络模型,也预测了目前被认为是未知信号糖的几种PMM2突变的致病性。 我们相信这个模型有助于减轻基因组医学中未知变异的负担。
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 ...