标题： CryoDyna：具有物理感知神经网络的冷冻电镜大分子动力学多尺度端到端建模 显示英文标题

标题： CryoDyna: Multiscale end-to-end modeling of cryo-EM macromolecule dynamics with physics-aware neural network

摘要： 单颗粒冷冻电镜已经改变了结构生物学，但在原子分辨率下解析构象异质性仍然面临挑战。 现有的冷冻电镜构象异质性分析方法要么缺乏原子细节，要么由于图像噪声和单视角信息有限而容易过拟合。 为了获得原子细节的多种构象并充分利用不同取向的粒子图像，我们提出了CryoDyna，这是一种深度学习框架，通过整合跨视角注意力和多尺度形变建模，直接从二维投影中推断大分子动态。 结合粗粒度MARTINI表示和原子回溯，CryoDyna实现了蛋白质构象景观的近原子级解释。 在多个模拟和实验数据集上验证后，CryoDyna展示了改进的建模精度，并能够稳健地恢复隐藏在冷冻电镜粒子堆中的多尺度复杂结构变化。 例如，我们生成了蛋白质-RNA协调运动，解析了RAG信号末端复合物未见区域的动力学，以一次拍摄的方式映射了移动核糖体状态，并揭示了膜锚定蛋白多聚体的逐步闭合过程。 这项工作弥合了冷冻电镜构象异质性分析与原子尺度结构动力学之间的差距，为探索复杂的生物机制提供了一个有前景的工具。 显示英文摘要

摘要： Single-particle cryo-EM has transformed structural biology but still faces challenges in resolving conformational heterogeneity at atomic resolution. Existing cryo-EM heterogeneity analysis methods either lack atomic details or tend to subject to overfitting due to image noise and limited information in single views. To obtain atomic detailed multiple conformations and make full use of particle images of different orientations, we present here CryoDyna, a deep learning framework to infer macromolecular dynamics directly from 2D projections by integrating cross-view attention and multi-scale deformation modeling. Combining coarse-grained MARTINI representation with atomic backmapping, CryoDyna achieves near-atomic interpretation of protein conformational landscapes. Validated on multiple simulated and experimental datasets, CryoDyna demonstrates improved modeling accuracy and robustly recovers multi-scale complex structure changes hidden in the cryo-EM particle stacks. As examples, we generated protein-RNA coordinated motions, resolved dynamics in the unseen region of RAG signal end complex, mapped translocating ribosome states in a one-shot manner, and revealed step-wise closure of a membrane-anchored protein multimer. This work bridges the gap between cryo-EM heterogeneity analysis and atomic-scale structural dynamics, offering a promising tool for exploration of complex biological mechanisms.