Automatic post-picking using MAPPOS improves particle image detection from Cryo-EM micrographs

TL;DR

MAPPOS is a machine learning-based post-picking tool that automates particle classification in cryo-EM micrographs, significantly reducing manual effort while maintaining high accuracy for large datasets.

Contribution

The paper introduces MAPPOS, a supervised machine learning method for automated particle classification in cryo-EM, improving efficiency over manual methods.

Findings

MAPPOS achieves human-like classification accuracy with only a few hundred training images.

It significantly reduces manual workload in cryo-EM particle analysis.

MAPPOS performs well on both simulated and real cryo-EM datasets.

Abstract

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.