CryoChains: Heterogeneous Reconstruction of Molecular Assembly of Semi-flexible Chains from Cryo-EM Images

TL;DR

CryoChains is a novel method that reconstructs 3D biomolecular structures from cryo-EM images by modeling large shape variations with physical plausibility, improving resolution and interpretability over existing methods.

Contribution

CryoChains introduces a biophysically-grounded approach combining rigid body transformations and normal mode analysis for efficient 3D reconstruction of flexible biomolecules.

Findings

Outperforms existing interpretable deep learning methods in resolution.

Provides physically meaningful quantification of molecular conformations.

Demonstrates effectiveness on synthetic data of GABA extsubscript{B} and heat shock proteins.

Abstract

Cryogenic electron microscopy (cryo-EM) has transformed structural biology by allowing to reconstruct 3D biomolecular structures up to near-atomic resolution. However, the 3D reconstruction process remains challenging, as the 3D structures may exhibit substantial shape variations, while the 2D image acquisition suffers from a low signal-to-noise ratio, requiring to acquire very large datasets that are time-consuming to process. Current reconstruction methods are precise but computationally expensive, or faster but lack a physically-plausible model of large molecular shape variations. To fill this gap, we propose CryoChains that encodes large deformations of biomolecules via rigid body transformation of their chains, while representing their finer shape variations with the normal mode analysis framework of biophysics. Our synthetic data experiments on the human GABA\textsubscript{B} and heat shock protein show that CryoChains gives a biophysically-grounded quantification of the heterogeneous conformations of biomolecules, while reconstructing their 3D molecular structures at an improved resolution compared to the current fastest, interpretable deep learning method.