Unsupervised particle sorting for high-resolution single-particle cryo-EM

TL;DR

This paper introduces an unsupervised particle sorting method for cryo-EM that enhances automation and reproducibility in high-resolution structure determination by using statistical models to classify particles.

Contribution

The study presents a novel unsupervised approach for particle sorting in cryo-EM, reducing reliance on subjective criteria and enabling fully automated data processing workflows.

Findings

Particles can be effectively sorted using a statistical model of refinement scores.

The method improves automation and reproducibility in cryo-EM data processing.

It facilitates high-resolution structure determination without expert intervention.

Abstract

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing steps, however, still require expert user intervention in order to converge to the correct high-resolution structure. In particular, strategies to identify homogeneous populations of particles rely heavily on subjective criteria that are not always consistent or reproducible among different users. Here, we explore the use of unsupervised strategies for particle sorting that are compatible with the autonomous operation of the image processing pipeline. More specifically, we show that particles can be successfully sorted based on a simple statistical model for the distribution of scores assigned during refinement. This represents an important step towards the development of automated workflows for protein structure determination using single-particle cryo-EM.