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Annual Review of Biomedical Data Science

Volume 3, 2020

Computational Methods for Single-Particle Electron Cryomicroscopy

Abstract

Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional (3D) structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the 3D molecular structure needs to be determined from many noisy 2D tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing, which also play a significant role in many other data science applications.

Keyword(s): conformational heterogeneity, contrast transfer function, electron cryomicroscopy, image alignment and classification, statistical estimation, three-dimensional tomographic reconstruction
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/content/journals/10.1146/annurev-biodatasci-021020-093826
2020-07-20
2025-04-20
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/content/journals/10.1146/annurev-biodatasci-021020-093826
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Computational Methods for Single-Particle Electron Cryomicroscopy
Annual Review of Biomedical Data Science 3, 163 (2020); https://doi.org/10.1146/annurev-biodatasci-021020-093826
/content/journals/10.1146/annurev-biodatasci-021020-093826
/content/journals/10.1146/annurev-biodatasci-021020-093826
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