用拓扑分析方法区分细菌鞭毛马达的转动与停滞状态。
Mathematical Insights into Protein Architecture: Persistent Homology and Machine Learning Applied to the Flagellar Motor
- 通过持久同调提取蛋白质结构的多尺度拓扑特征。
- 在多种细菌数据上实现高精度分类，对结构差异鲁棒。
- 适合研究蛋白功能与结构关系的计算生物学家。
我们提出一种机器学习方法，利用持久同调将细菌鞭毛马达分为旋转和停滞两种功能状态。通过将蛋白质结构数据嵌入拓扑框架，从原子坐标构建的过滤单纯复形中提取多尺度特征。这些拓扑不变量，特别是持久图和条形码，捕捉了与马达功能相关的几何与连通性模式。提取的特征被向量化并集成到包含降维和监督分类的机器学习流程中。该方法应用于涵盖多种细菌物种的实验表征鞭毛马达数据集，表现出高分类准确率且对结构变化具有鲁棒性。结果表明，拓扑数据分析能揭示传统几何描述无法触及的功能相关模式，为蛋白质功能预测提供新计算工具。
原文摘要 · Abstract (English)
We present a machine learning approach that leverages persistent homology to classify bacterial flagellar motors into two functional states: rotated and stalled. By embedding protein structural data into a topological framework, we extract multiscale features from filtered simplicial complexes constructed over atomic coordinates. These topological invariants, specifically persistence diagrams and barcodes, capture critical geometric and connectivity patterns that correlate with motor function. The extracted features are vectorized and integrated into a machine learning pipeline that includes dimensionality reduction and supervised classification. Applied to a curated dataset of experimentally characterized flagellar motors from diverse bacterial species, our model demonstrates high classification accuracy and robustness to structural variation. This approach highlights the power of topological data analysis in revealing functionally relevant patterns beyond the reach of traditional geometric descriptors, offering a novel computational tool for protein function prediction.
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