用分子动力学模拟提升AI对基因突变致病性的预测准确率
Dynamicasome: a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations
- 结合分子动力学模拟的构象数据训练AI模型
- 在PMM2基因突变预测上显著优于现有工具
- 可为临床未知意义突变提供可靠判断依据
基因组医学的发展加速了疾病相关基因中突变的发现，但许多突变的致病性仍不明确，阻碍其在诊断和临床决策中的应用。现有的预测AI模型在功能验证数据集上的表现不佳。本研究通过将从分子动力学模拟（MDS）提取的详细构象数据融入先进AI模型，显著提升了预测能力。我们对疾病基因PMM2进行了全面的突变分析，对每种变异体的结构模型进行MDS，并基于该数据集训练的AI模型，在预测已知致病性突变时表现优于现有工具。其中表现最佳的神经网络模型还成功预测了多个目前被归类为意义未明的PMM2突变。我们认为该模型有助于缓解基因组医学中未知变异带来的负担。
原文摘要 · Abstract (English)
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.
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