发表机构
MFM Lab, TUM; AMC, TUM（慕尼黑工业大学MFM实验室; 慕尼黑工业大学AMC）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SupraTITO提出可迁移生成分子动力学框架，通过隐式传递算子学习肽自组装，泛化至未见序列与稀薄浓度，准确再现结构及时间演化。
AI 中文摘要
肽序列既决定了通过超分子组装形成的结构，也决定了这些结构出现的动力学过程，但预测这两者都需要解析许多相互作用分子之间的缓慢集体过程。分子动力学（MD）为这些过程提供了微观层面的洞察，然而组装的长时标和庞大的肽序列空间使得系统性探索在计算上要求极高。我们引入了SupraTITO，一个用于超分子系统的可迁移生成分子动力学（GenMD）框架，并通过肽自组装进行了演示。SupraTITO学习以肽序列、分子拓扑和周期性几何为条件的可迁移隐式传递算子（TITO），使得构型能够在比MD积分步长长得多的物理区间内传播。在一个全面的二肽基准测试中，SupraTITO泛化到未见的序列，并再现了序列依赖的结构和动力学，同时在长程滚动中保持分子完整性。与在同一轨迹数据上训练的直接系综预测相比，SupraTITO更准确地再现了组装结构，同时解析了其时间演化。学习到的动力学在肽浓度上具有泛化能力，包括训练中未代表的稀薄条件。这些结果将可迁移GenMD扩展到周期性超分子系统中的集体动力学，并为建模肽组装之外的相关过程提供了基础。
英文摘要
Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requires resolving slow collective processes among many interacting molecules. Molecular dynamics (MD) provides microscopic insight into these processes, yet the long timescales of assembly and the vast peptide sequence space make systematic exploration computationally demanding. We introduce SupraTITO, a transferable generative molecular dynamics (GenMD) framework for supramolecular systems, demonstrated through peptide self-assembly. SupraTITO learns transferable implicit transfer operators (TITO) conditioned on peptide sequence, molecular topology, and periodic geometry, allowing configurations to be propagated over physical intervals much longer than an MD integration step. On a comprehensive dipeptide benchmark, SupraTITO generalizes to held-out sequences and reproduces sequence-dependent structures and dynamics while maintaining molecular integrity over long rollouts. Compared with direct ensemble prediction trained on the same trajectory data, SupraTITO more accurately reproduces assembly structures while also resolving their temporal evolution. The learned dynamics generalize across peptide concentrations, including dilute conditions not represented during training. These results extend transferable GenMD to collective dynamics in periodic supramolecular systems and provide a foundation for modeling related processes beyond peptide assembly.
