Robust ab initio solution of the cryo-EM reconstruction problem at low resolution with small data sets

TL;DR

This paper introduces a robust, automatic, and fast ab initio algorithm for low-resolution cryo-EM reconstruction from small data sets, aiding real-time particle quality assessment during microscopy.

Contribution

The paper presents a novel algorithm capable of ab initio cryo-EM reconstruction from small datasets, improving robustness and speed over existing methods.

Findings

Successfully reconstructs low-resolution models from few thousand particles

Operates automatically and robustly with noisy data

Potential for real-time particle quality assessment

Abstract

Single particle cryo-electron microscopy has become a critical tool in structural biology over the last decade, able to achieve atomic scale resolution in three dimensional models from hundreds of thousands of (noisy) two-dimensional projection views of particles frozen at unknown orientations. This is accomplished by using a suite of software tools to (i) identify particles in large micrographs, (ii) obtain low-resolution reconstructions, (iii) refine those low-resolution structures, and (iv) finally match the obtained electron scattering density to the constituent atoms that make up the macromolecule or macromolecular complex of interest. Here, we focus on the second stage of the reconstruction pipeline: obtaining a low resolution model from picked particle images. Our goal is to create an algorithm that is capable of ab initio reconstruction from small data sets (on the order of a few thousand selected particles). More precisely, we propose an algorithm that is robust, automatic, and fast enough that it can potentially be used to assist in the assessment of particle quality as the data is being generated during the microscopy experiment.