用拓扑学习捕捉蛋白多层级结构，提升功能预测精度。
Topotein: Topological Deep Learning for Protein Representation Learning
- 构建蛋白组合复形PCC，分层表示残基到完整蛋白的几何结构
- 引入SE(3)等变消息传递网络，在折叠分类任务上超越现有模型
- 适合研究蛋白结构-功能关系或需要多尺度分析的生物信息学工作者
蛋白质表征学习（PRL）对理解结构-功能关系至关重要，但现有基于序列或图的方法难以捕捉蛋白质结构固有的层次组织。我们提出Topotein框架，通过新型蛋白组合复形（PCC）和拓扑完备感知机网络（TCPNet），将拓扑深度学习应用于PRL。PCC在多个层次（从残基到二级结构再到完整蛋白质）表示蛋白质，同时保留各层次的几何信息。TCPNet在这些层次结构上使用SE(3)-等变消息传递，更有效捕获多尺度结构模式。在四个PRL任务上的广泛实验表明，TCPNet持续优于当前最先进的几何图神经网络。该方法在需理解二级结构排列的任务（如折叠分类）中表现尤为突出，验证了层次拓扑特征在蛋白质分析中的重要性。
原文摘要 · Abstract (English)
Protein representation learning (PRL) is crucial for understanding structure-function relationships, yet current sequence- and graph-based methods fail to capture the hierarchical organization inherent in protein structures. We introduce Topotein, a comprehensive framework that applies topological deep learning to PRL through the novel Protein Combinatorial Complex (PCC) and Topology-Complete Perceptron Network (TCPNet). Our PCC represents proteins at multiple hierarchical levels -- from residues to secondary structures to complete proteins -- while preserving geometric information at each level. TCPNet employs SE(3)-equivariant message passing across these hierarchical structures, enabling more effective capture of multi-scale structural patterns. Through extensive experiments on four PRL tasks, TCPNet consistently outperforms state-of-the-art geometric graph neural networks. Our approach demonstrates particular strength in tasks such as fold classification which require understanding of secondary structure arrangements, validating the importance of hierarchical topological features for protein analysis.
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