3DFlex: determining structure and motion of flexible proteins from cryo-EM

TLDR

Modeling flexible macromolecules remains a major challenge in single‑particle cryo‑EM, yet it holds the promise of revealing fundamental structural biology questions. The study introduces 3DFlex, a motion‑based neural network that derives high‑resolution 3D density and an explicit motion model of flexible proteins directly from 2D cryo‑EM images. 3DFlex treats conformational variability as physical density transport that preserves local geometry, enabling continuous heterogeneity refinement through a neural network framework. Applied to large complexes and small flexible proteins, 3DFlex learns nonrigid motions, resolves moving secondary‑structure elements, and surpasses existing methods by improving 3D density resolution via coherent signal across the conformational landscape.

Abstract

Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein's motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape.