  Cryo-electron microscopy (cryo-EM) studies using single particle
reconstruction are extensively used to reveal structural information on
macromolecular complexes. Aiming at the highest achievable resolution, state of
the art electron microscopes automatically acquire thousands of high-quality
micrographs. Particles are detected on and boxed out from each micrograph using
fully- or semi-automated approaches. However, the obtained particles still
require laborious manual post-picking classification, which is one major
bottleneck for single particle analysis of large datasets. We introduce MAPPOS,
a supervised post-picking strategy for the classification of boxed particle
images, as additional strategy adding to the already efficient automated
particle picking routines. MAPPOS employs machine learning techniques to train
a robust classifier from a small number of characteristic image features. In
order to accurately quantify the performance of MAPPOS we used simulated
particle and non-particle images. In addition, we verified our method by
applying it to an experimental cryo-EM dataset and comparing the results to the
manual classification of the same dataset. Comparisons between MAPPOS and
manual post-picking classification by several human experts demonstrated that
merely a few hundred sample images are sufficient for MAPPOS to classify an
entire dataset with a human-like performance. MAPPOS was shown to greatly
accelerate the throughput of large datasets by reducing the manual workload by
orders of magnitude while maintaining a reliable identification of non-particle
images.

Machine Learning;Computer Vision and Pattern Recognition