用统一模型生成跨时间尺度的蛋白质动态，高效模拟构象变化。
DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales
- 三注意力机制融合几何、结构与时间一致性建模
- 在微秒级轨迹上保持自由能景观与稳态分布准确
- 适合研究变构、配体结合等动态过程的科研人员
蛋白质功能依赖于多时空尺度的协同运动，涉及配体结合、别构效应和催化等过程。然而，通过分子动力学（MD）模拟获取长时间尺度构象变化对系统探索多样体系仍成本高昂。本文提出DyneTrion，一种联合施加几何对称性、结构一致性和时间相干性的生成式蛋白质动态模拟器。该模型采用三注意力架构，集成不变点注意力（IPA）实现SE(3)鲁棒的几何更新，以参考构象为锚点的空间注意力维持结构完整性，以及建模时间帧间关联演化的时序注意力。在100纳秒级MD轨迹基准测试中，DyneTrion重现了MD导出的柔韧性、构象分布和相互作用可观测量，并在外推过程中保持立体化学有效性。为评估长时间尺度泛化能力，我们构建dynamicPDB数据集，包含超过10,000个蛋白质，具备长达1微秒、10皮秒分辨率的全原子轨迹及物理标注。在微秒级轨迹上，DyneTrion保持自由能景观与亚稳态种群分布，支持从无配体到有配体转变中的大尺度构象传播及快速折叠过程。DyneTrion为从静态结构预测迈向时间分辨、集合忠实的蛋白质建模提供了可扩展路径。代码已公开于https://github.com/fudan-generative-vision/DyneTrion。
原文摘要 · Abstract (English)
Proteins function through coordinated motion across multiple spatial and temporal scales, underpinning processes such as ligand binding, allostery, and catalysis. However, accessing long-timescale conformational change through molecular dynamics (MD) simulations remains prohibitively expensive for systematic exploration across diverse systems. Here, we present DyneTrion, a generative protein dynamics emulator that jointly enforces geometric symmetry, structural consistency and temporal coherence within a single framework. DyneTrion uses a tri-attention architecture that integrates invariant point attention (IPA) for SE(3)-robust geometric updates, spatial attention anchored to a reference conformation to preserve structural integrity, and temporal attention to model correlated evolution across time frames. Across 100-ns MD trajectory simulation benchmarks, DyneTrion reproduces MD-derived flexibility, ensemble distributions and interaction observables while maintaining stereochemical validity during extrapolation. To evaluate long time-scale generalization, we introduce dynamicPDB, a dataset of over 10,000 proteins with up to 1-$μ$s all-atom trajectories at 10-ps resolution and accompanying physical annotations. On microsecond trajectories, DyneTrion preserves free-energy landscapes and metastable-state populations, and it supports large conformational propagation in apo-to-holo transitions and fast folders. Together, DyneTrion provides a scalable path from static structure prediction toward time-resolved, ensemble-faithful protein modeling. The code is publicly available at https://github.com/fudan-generative-vision/DyneTrion
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。