用多尺度高斯模型直接从静态结构预测蛋白动态特性
Learning residue level protein dynamics with multiscale Gaussians
- 基于多变量高斯建模，分残基和成对两个尺度预测动态
- 准确预测残基柔韧性（RMSF），并重建全协方差矩阵
- 参数量远少于以往方法，适合大规模动态分析
许多方法已能预测蛋白质的静态结构，但理解其结构动态对揭示生物功能至关重要。尽管分子动力学（MD）模拟仍是计算领域的金标准，但其高昂的计算成本限制了可扩展性。我们提出DynaProt，一种轻量级、SE(3)-不变的框架，可直接从静态结构预测丰富的蛋白动态描述符。通过多变量高斯视角建模，DynaProt在两个互补尺度上估计动态：(1) 残基级别的局部柔韧性，以$3 imes 3$协方差矩阵表示；(2) 残基间的联合标量协方差，刻画动态耦合。由此输出可实现高精度的残基水平柔韧性（RMSF）预测，并惊人地实现全协方差矩阵的合理重构，用于快速生成构象集合。尤为关键的是，其参数量相比以往方法减少数个数量级。结果表明，直接预测蛋白动态是现有方法的高效替代方案。
原文摘要 · Abstract (English)
Many methods have been developed to predict static protein structures, however understanding the dynamics of protein structure is essential for elucidating biological function. While molecular dynamics (MD) simulations remain the in silico gold standard, its high computational cost limits scalability. We present DynaProt, a lightweight, SE(3)-invariant framework that predicts rich descriptors of protein dynamics directly from static structures. By casting the problem through the lens of multivariate Gaussians, DynaProt estimates dynamics at two complementary scales: (1) per-residue marginal anisotropy as $3 \times 3$ covariance matrices capturing local flexibility, and (2) joint scalar covariances encoding pairwise dynamic coupling across residues. From these dynamics outputs, DynaProt achieves high accuracy in predicting residue-level flexibility (RMSF) and, remarkably, enables reasonable reconstruction of the full covariance matrix for fast ensemble generation. Notably, it does so using orders of magnitude fewer parameters than prior methods. Our results highlight the potential of direct protein dynamics prediction as a scalable alternative to existing methods.
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