The dynamics of biomolecules are crucial for our understanding of their functioning in living systems. However, current 3D imaging techniques, such as cryogenic electron microscopy (cryo-EM), require freezing the sample, which limits the observation of their conformational changes in real time. The innovative liquid-phase electron microscopy (liquid-phase EM) technique allows molecules to be placed in the native liquid environment, providing a unique opportunity to observe their dynamics. In this paper, we propose TEMPOR, a Temporal Electron MicroscoPy Object Reconstruction algorithm for liquid-phase EM that leverages an implicit neural representation (INR) and a dynamical variational auto-encoder (DVAE) to recover time series of molecular structures. We demonstrate its advantages in recovering different motion dynamics from two simulated datasets, 7bcq and Cas9. To our knowledge, our work is the first attempt to directly recover 3D structures of a temporally-varying particle from liquid-phase EM movies. It provides a promising new approach for studying molecules' 3D dynamics in structural biology.

翻译：生物分子的动力学对于我们理解其在生命系统中的功能至关重要。然而，当前的3D成像技术，如冷冻电子显微镜（cryo-EM），需要冷冻样品，限制了实时观察其构象变化的能力。创新的液相电子显微镜（液相EM）技术允许将分子置于原生液体环境中，为观察其动力学提供了独特机会。本文提出了TEMPOR（时间电子显微镜物体重建算法），这是一种用于液相EM的算法，利用隐式神经表示（INR）和动态变分自编码器（DVAE）来恢复分子结构的时序序列。我们通过在两个模拟数据集（7bcq和Cas9）上的实验，展示了该算法在恢复不同运动动力学方面的优势。据我们所知，我们的工作是首次尝试直接从液相EM视频中恢复随时间变化粒子的3D结构，为结构生物学中研究分子的三维动力学提供了一种有前景的新方法。