发表机构
Imperial College London; The Chinese University of Hong Kong; Technical University of Munich（帝国理工学院; 香港中文大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RNADynBench提供2585条标准化RNA动力学轨迹，RNADynNet通过统一模型实现轨迹生成与动力学指纹提取，物理基础提升性能，RMSF相关性达0.875和0.766。
AI 中文摘要
核糖核酸（RNA）通过构象变化发挥功能，而这些变化并未被静态结构完全捕捉。然而，大规模标准化的RNA动力学数据仍然有限，现有方法通常将轨迹生成和动力学理解视为独立目标。在此，我们引入RNADynBench，一个标准化的RNA分子动力学（MD）基准，包含2585条经过质量控制的100纳秒全原子轨迹和泄漏控制的数据划分。基于RNADynBench，我们开发了RNADynNet，一个用于RNA动力学学习的统一模型，该模型使用共享主干网络，从单个构象同时进行轨迹生成和动力学指纹提取。它结合了坐标去噪、单帧到轨迹对齐以及物理基础，将全原子轨迹生成与动力学表示学习联系起来。物理基础改善了生成的动力学以及从这些指纹中可恢复的物理信息。在两个测试集（包括高灵活性挑战集）中，生成的轨迹实现了0.875和0.766的RMSF相关性，而单构象预测与MD衍生的动力学显示出相当的吻合度。RNADynBench和RNADynNet共同建立了一个用于生成和理解RNA动力学的基准和统一建模框架。
英文摘要
Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures. However, large-scale standardized RNA dynamics data remain limited, and existing approaches typically treat trajectory generation and dynamics understanding as separate objectives. Here, we introduce RNADynBench, a standardized RNA molecular dynamics (MD) benchmark with 2585 quality-controlled 100-ns all-atom trajectories and leakage-controlled splits. Building on RNADynBench, we develop RNADynNet, a unified model for RNA dynamics learning that uses a shared backbone for both trajectory generation and dynamics fingerprint extraction from a single conformer. It combines coordinate denoising, single-frame-to-trajectory alignment, and physical grounding to connect all-atom trajectory generation with dynamics representation learning. Physical grounding improves both generated dynamics and the physical information recoverable from these fingerprints. Across both test sets, including the high-flexibility challenge set, the generated trajectories achieve RMSF correlations of 0.875 and 0.766, while single-conformer predictions show comparable agreement with MD-derived dynamics. RNADynBench and RNADynNet together establish a benchmark and unified modeling framework for generating and understanding RNA dynamics.
