Confidence-guided cryo-EM map optimisation with LocScale-2.0
- 1Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Delft, The Netherlands
- 2Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Delft, The Netherlands. [email protected]
Abstract
Cryogenic sample electron microscopy (cryo-EM) maps often display uneven quality, with high-resolution features coexisting alongside weak or poorly ordered regions. Such variation complicates structural interpretation, especially for heterogeneous macromolecular assemblies. Here, we present LocScale-2.0, a context-aware map optimisation framework that operates without prior knowledge of molecular structure or composition. By leveraging general expectations of electron scattering by biological macromolecules, it enhances local detail and connectivity while preserving weak but biologically relevant structural context. We further introduce LocScale-FEM, a Bayesian approximate deep-learning approach that emulates this optimisation to generate feature-enhanced maps. LocScaleFEM provides voxel-wise confidence scores that give a statistically grounded measure of reliability, are sensitive to local phase error in the feature-enhanced map, and highlight regions where interpretation warrants caution. Selected examples illustrate how confidence-guided map optimisation can aid biological interpretation and increase objectivity in cryo-EM density analysis.
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BibTeX
@article{bharadwaj2026confidenceguided,
title = {{Confidence-guided cryo-EM map optimisation with LocScale-2.0}},
author = {Alok Bharadwaj and Reinier De Bruin and Arjen J. Jakobi},
journal = {Nature Communications},
year = {2026},
month = jul,
doi = {10.1038/s41467-026-75327-8},
url = {https://doi.org/10.1038/s41467-026-75327-8},
note = {CC BY},
}
RIS
TY - JOUR
TI - Confidence-guided cryo-EM map optimisation with LocScale-2.0
AU - Alok Bharadwaj
AU - Reinier De Bruin
AU - Arjen J. Jakobi
PY - 2026
DA - 2026/07/17
JO - Nature Communications
DO - 10.1038/s41467-026-75327-8
UR - https://doi.org/10.1038/s41467-026-75327-8
AB - Cryogenic sample electron microscopy (cryo-EM) maps often display uneven quality, with high-resolution features coexisting alongside weak or poorly ordered regions. Such variation complicates structural interpretation, especially for heterogeneous macromolecular assemblies. Here, we present LocScale-2.0, a context-aware map optimisation framework that operates without prior knowledge of molecular structure or composition. By leveraging general expectations of electron scattering by biological macromolecules, it enhances local detail and connectivity while preserving weak but biologically relevant structural context. We further introduce LocScale-FEM, a Bayesian approximate deep-learning approach that emulates this optimisation to generate feature-enhanced maps. LocScaleFEM provides voxel-wise confidence scores that give a statistically grounded measure of reliability, are sensitive to local phase error in the feature-enhanced map, and highlight regions where interpretation warrants caution. Selected examples illustrate how confidence-guided map optimisation can aid biological interpretation and increase objectivity in cryo-EM density analysis.
SN - 2041-1723
ER -