CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy.

ABSTRACT: Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determination. Here, we present a generalized deep learning tool, CASSPER, for the automated detection and isolation of protein particles in transmission microscope images. This deep learning tool uses Semantic Segmentation and a collection of visually prepared training samples to capture the differences in the transmission intensities of protein, ice, carbon, and other impurities found in the micrograph. CASSPER is a semantic segmentation based method that does pixel-level classification and completely eliminates the need for manual particle picking

SUBMITTER: George B 

PROVIDER: S-EPMC7884729 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature