Computational tools for heterogeneity analysis allow studying protein conformational variability in the cellular environment.

3D classification of subtomograms represents a versatile tool for probing protein conformational variability landscapes.

Constrained classification of 2D projection sets is a promising strategy for studying discrete and continuous variability.

Particle alignment combined with discrete classification enables high-resolution heterogeneity analysis in situ.

Structural variability plays a crucial role in enabling biological function, as the ability of proteins to adopt multiple conformations allows them to perform diverse cellular tasks. Cryo-electron tomography combined with subtomogram averaging and classification has emerged as a powerful technique for elucidating the conformational dynamics of proteins in their near-native environment. Increased data availability has provided a driving force for improvements in image classification algorithms which have enabled conformational heterogeneity studies of proteins in situ at higher resolutions than previously possible. In particular, the use of 2D particle projections extracted from raw tilt-series paired with constrained classification strategies of projection sets has emerged as a promising strategy for classifying particles in 3D. Despite these efforts, further method development will be needed to extend the applicability of current strategies for 3D classification to more challenging biological targets, including low-molecular weight complexes and membrane proteins.

© 2025 The Author. Published by Elsevier Ltd.