用深度学习解析蛋白质在微秒级运动的构象图谱
ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
- 结合几何散射变换与Transformer注意力机制捕捉蛋白动态
- 可生成具有时间一致性的蛋白质轨迹潜在表示
- 适合研究蛋白构象变化与功能关系的生物学家
理解蛋白质结构的动态特性对揭示其生物学功能至关重要。尽管静态折叠结构预测已取得显著进展，但在微秒至毫秒尺度上建模蛋白质运动仍具挑战。为此，我们提出一种新型深度学习架构——蛋白质散射注意力嵌入模型（ProtSCAPE），该模型融合几何散射变换与基于Transformer的注意力机制，从分子动力学（MD）模拟中捕获蛋白质动态。ProtSCAPE利用几何散射变换的多尺度特性，将蛋白质结构视为图进行特征提取，并通过双注意力结构分别关注残基和氨基酸信号，生成蛋白质轨迹的潜在表示。此外，模型引入回归头以确保潜在表示的时间一致性。
原文摘要 · Abstract (English)
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.
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