用物理先验提升阿尔茨海默病蛋白构象分区准确性
PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories
- 融合半径和溶剂可及表面积等物理先验构建拓扑特征
- 在Aβ42数据集上实现更优的稳定状态划分效果
- 提供可视化交互平台，适合生物研究者使用
理解β-淀粉样蛋白（Aβ），特别是Aβ42异构体的构象演化，对揭示阿尔茨海默病的致病机制至关重要。然而，现有端到端深度学习模型常因缺乏显式物理约束，难以捕捉蛋白质轨迹中的细微状态转换。本文提出PIS——一种基于物理信息的系统，用于鲁棒的代谢稳定状态分区。通过将预先计算的物理先验（如半径、溶剂可及表面积）融入拓扑特征提取，该模型在Aβ42数据集上表现优异。此外，PIS提供交互式平台，支持物理特性的动态监控与多维结果验证，为生物研究人员提供了具备物理可解释性的分析工具。演示视频见https://youtu.be/AJHGzUtRCg0。
原文摘要 · Abstract (English)
Understanding the conformational evolution of $β$-amyloid ($Aβ$), particularly the $Aβ_{42}$ isoform, is fundamental to elucidating the pathogenic mechanisms underlying Alzheimer's disease. However, existing end-to-end deep learning models often struggle to capture subtle state transitions in protein trajectories due to a lack of explicit physical constraints. In this work, we introduce PIS, a Physics-Informed System designed for robust metastable state partitioning. By integrating pre-computed physical priors, such as the radius of gyration and solvent-accessible surface area, into the extraction of topological features, our model achieves superior performance on the $Aβ_{42}$ dataset. Furthermore, PIS provides an interactive platform that features dynamic monitoring of physical characteristics and multi-dimensional result validation. This system offers biological researchers a powerful set of analytical tools with physically grounded interpretability. A demonstration video of PIS is available on https://youtu.be/AJHGzUtRCg0.
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