Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Movies

TL;DR

This paper introduces TEMPOR, a novel algorithm combining neural representations and auto-encoders to recover 3D molecular dynamics from liquid-phase electron microscopy movies, enabling real-time observation of biomolecular conformational changes.

Contribution

It is the first method to directly reconstruct 3D structures of dynamic molecules from liquid-phase EM data, advancing real-time structural biology studies.

Findings

Successfully recovered motion dynamics from simulated datasets

Demonstrated advantages over existing methods in 3D dynamic reconstruction

First to recover 3D structures of temporally-varying particles from liquid-phase EM

Abstract

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.