Abstract
Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a three-dimensional Coulomb potential density map. Newly developed algorithms for data analysis allow for the extraction of structural heterogeneity from a massive and low signal-to-noise-ratio (SNR) cryo-EM dataset, expanding our understanding of multiple conformational states, or further implications in dynamics, of the target biomolecule. This review provides an overview that briefly describes the workflow of single-particle cryo-EM, including imaging and data processing, and new methods developed for analyzing the data heterogeneity to understand the structural variability of biomolecules.
Original language | English (US) |
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Article number | 628 |
Journal | Biomolecules |
Volume | 12 |
Issue number | 5 |
DOIs | |
State | Published - May 2022 |
Keywords
- deep learning
- heterogeneity
- image classification
- molecular dynamics
- molecular dynamics flexible fitting
- single-particle cryo-EM
ASJC Scopus subject areas
- Biochemistry
- Molecular Biology