迈向基于几何二级结构模样的多尺度蛋白质学习
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
AI总结：
本文提出了一种针对蛋白质的多尺度图学习框架，通过分层图结构和双GNN模型提升结构预测精度并降低计算成本。
摘要：
图神经网络（GNNs）已逐渐成为通过捕捉残基层面的空间关系来学习蛋白质结构的强大工具。然而，现有的基于GNN的方法在学习多尺度表示和高效建模长程依赖关系方面常常面临挑战。在本工作中，我们提出了一种针对蛋白质的高效多尺度图学习框架。我们提出的方法包含两个关键组成部分：（1）它构建了一个分层图表示，包括一组细粒度的子图，每个对应于一个二级结构模（例如，α-螺旋、β-片层、环），以及一个粗粒度的图，该图通过这些模的空间排列和相对取向来连接它们。（2）它使用两个GNN进行特征学习：第一个在个体二级模内操作以捕捉局部相互作用，第二个在模之间建模更高层的结构关系。我们的模块化框架允许在每个阶段灵活选择GNN。理论上，我们证明了我们的分层框架保留了所需的最大表达性，确保没有丢失关键的结构信息。经验上，我们证明了将基线GNNs整合到我们的多尺度框架中显著提高了预测精度，并在各种基准上减少了计算成本。
英文摘要：
Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based methods often face challenges in learning multiscale representations and modeling long-range dependencies efficiently. In this work, we propose an efficient multiscale graph-based learning framework tailored to proteins. Our proposed framework contains two crucial components: (1) It constructs a hierarchical graph representation comprising a collection of fine-grained subgraphs, each corresponding to a secondary structure motif (e.g., $α$-helices, $β$-strands, loops), and a single coarse-grained graph that connects these motifs based on their spatial arrangement and relative orientation. (2) It employs two GNNs for feature learning: the first operates within individual secondary motifs to capture local interactions, and the second models higher-level structural relationships across motifs. Our modular framework allows a flexible choice of GNN in each stage. Theoretically, we show that our hierarchical framework preserves the desired maximal expressiveness, ensuring no loss of critical structural information. Empirically, we demonstrate that integrating baseline GNNs into our multiscale framework remarkably improves prediction accuracy and reduces computational cost across various benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
