用深度签名框架分析蛋白质动态，揭示复杂分子相互作用机制。
Deep Signature: Characterization of Large-Scale Molecular Dynamics
- 结合软谱聚类与签名变换，从轨迹中提取协同动力学特征。
- 在三个生物基准上优于现有方法，准确捕捉非光滑交互动态。
- 适合研究蛋白质功能机制与药物设计的科研人员使用。
理解蛋白质动态对揭示其功能机制及开发分子疗法至关重要。然而，生物过程中的高维复杂动态与原子间相互作用给现有计算技术带来巨大挑战。本文首次提出 Deep Signature，一种可计算的新型框架，基于演化轨迹表征复杂动态与原子间相互作用。该方法结合软谱聚类，局部聚合协作动力学以降低系统规模；并引入签名变换，通过迭代积分实现对非光滑交互动态的全局刻画。理论分析表明，Deep Signature 具备平移不变性、近似旋转不变性、坐标排列等变性以及时间重参数化不变性。实验在三个生物过程基准上验证，其性能显著优于基线方法。
原文摘要 · Abstract (English)
Understanding protein dynamics are essential for deciphering protein functional mechanisms and developing molecular therapies. However, the complex high-dimensional dynamics and interatomic interactions of biological processes pose significant challenge for existing computational techniques. In this paper, we approach this problem for the first time by introducing Deep Signature, a novel computationally tractable framework that characterizes complex dynamics and interatomic interactions based on their evolving trajectories. Specifically, our approach incorporates soft spectral clustering that locally aggregates cooperative dynamics to reduce the size of the system, as well as signature transform that collects iterated integrals to provide a global characterization of the non-smooth interactive dynamics. Theoretical analysis demonstrates that Deep Signature exhibits several desirable properties, including invariance to translation, near invariance to rotation, equivariance to permutation of atomic coordinates, and invariance under time reparameterization. Furthermore, experimental results on three benchmarks of biological processes verify that our approach can achieve superior performance compared to baseline methods.
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