Conformations of Macromolecules and their Complexes from Heterogeneous Datasets

TL;DR

This paper introduces advanced algorithms for analyzing large, heterogeneous datasets to determine the structures and conformations of macromolecules and their complexes, effectively capturing conformational heterogeneity without prior classification.

Contribution

The authors present a novel algorithmic framework capable of mapping macromolecular conformations directly from heterogeneous data without prior sorting or classification.

Findings

Algorithms successfully determine conformational spectra from diverse datasets

Applicable to cryo-EM and X-ray laser data

Effective for systems difficult to purify or crystallize

Abstract

We describe a new generation of algorithms capable of mapping the structure and conformations of macromolecules and their complexes from large ensembles of heterogeneous snapshots, and demonstrate the feasibility of determining both discrete and continuous macromolecular conformational spectra. These algorithms naturally incorporate conformational heterogeneity without resort to sorting and classification, or prior knowledge of the type of heterogeneity present. They are applicable to single-particle diffraction and image datasets produced by X-ray lasers and cryo-electron microscopy, respectively, and particularly suitable for systems not easily amenable to purification or crystallization.