Maskiton: Interactive, web-based classification of single-particle electron microscopy images

Area of Science:

• Structural biology
• Biophysics
• Computational biology

Background:

• Electron microscopy (EM) is crucial for analyzing biological macromolecule structures.
• Classifying heterogeneous EM samples is vital for high-resolution analysis and identifying conformations.

Purpose of the Study:

• To introduce Maskiton, an interactive, web-based tool for custom mask creation and 2D classification of single-particle EM images.
• To streamline the process of exploring structural heterogeneity in EM datasets.

Main Methods:

• Developed an interactive, browser-based tool, Maskiton.
• Implemented features such as resumable uploads, custom mask creation, progress updates, and interactive result viewing.
• Applied Maskiton to experimental datasets including Ltn1 E3 ubiquitin ligase, 30S ribosomal subunit assembly, and Immunoglobulin M.

Main Results:

• Maskiton facilitates easier and faster exploration of heterogeneity in single-particle EM datasets.
• Demonstrated the tool's utility on diverse biological samples, aiding in structural analysis.
• Showcased complexity reduction in classification for complex molecules like Immunoglobulin M.

Conclusions:

• Maskiton significantly improves the efficiency and accessibility of 2D classification for EM data.
• The tool serves as a proof-of-concept for developing future interactive EM data processing interfaces.
• Maskiton empowers researchers to better understand macromolecular structure and conformational dynamics.