  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.
