用分层图神经网络高效分析上千残基的分子动力学模拟，单卡分钟级完成。
Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
- 构建分层几何深度学习框架，聚合局部信息降低计算开销
- 千残基蛋白-核酸复合物模拟可在单个GPU上分钟级完成分析
- 适用于大规模生物分子系统，提升性能与结果可解释性
分子动力学模拟可生成复杂系统的原子级轨迹，但当系统缺乏成熟定量描述符时，分析仍具挑战。基于原子空间邻近关系传递消息的图神经网络（GNN）有望避免手工特征工程，然而在超过数百残基的生物分子系统中，因难以捕捉长程相互作用及大图带来的内存与运行时间开销，应用受限。本文提出通过局部信息聚合，在不损失原子细节的前提下显著降低内存与运行时间。实验表明，该方法使数千残基的蛋白-核酸复合物模拟可在单个GPU上于几分钟内完成分析；对于数百残基系统，有足够数据支持定量比较时，该方法在性能与可解释性上均有提升。
原文摘要 · Abstract (English)
Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Graph neural networks (GNNs) in which messages are passed between nodes that represent atoms that are spatial neighbors promise to obviate manual feature engineering, but the use of GNNs with biomolecular systems of more than a few hundred residues has been limited in the context of analyzing dynamics by both difficulties in capturing the details of long-range interactions with message passing and the memory and runtime requirements associated with large graphs. Here, we show how local information can be aggregated to reduce memory and runtime requirements without sacrificing atomic detail. We demonstrate that this approach opens the door to analyzing simulations of protein-nucleic acid complexes with thousands of residues on single GPUs within minutes. For systems with hundreds of residues, for which there are sufficient data to make quantitative comparisons, we show that the approach improves performance and interpretability.
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