Improved metrics for comparing structures of macromolecular assemblies determined by 3D electron-microscopy

By Agnel Praveen Joseph, Ingvar C. Lagerstedt, Ardan Patwardhan, Maya Topf, Martyn D. Winn. 2017

51 citations recorded by the source corpus.

Recent developments in 3-dimensional electron microcopy (3D-EM) techniques and a concomitant drive to look at complex molecular structures, have led to a rapid increase in the amount of volume data available for biomolecules. This creates a demand for better methods to analyse the data, including improved scores for comparison, classification and integration of data at different resolutions. To this end, we developed and evaluated a set of scoring functions that compare 3D-EM volumes. To test our scores we used a benchmark set of volume alignments derived from the Electron Microscopy Data Bank. We find that the performance of different scores vary with the map-type, resolution and the extent of overlap between volumes. Importantly, adding the overlap information to the local scoring functions can significantly improve their precision and accuracy in a range of resolutions. A combined score involving the local mutual information and overlap (LMI_OV) performs best overall, irrespective of the map category, resolution or the extent of overlap, and we recommend this score for general use. The local mutual information score itself is found to be more discriminatory than cross-correlation coefficient for intermediate-to-low resolution maps or when the map size and density distribution differ significantly. For comparing map surfaces, we implemented two filters to detect the surface points, including one based on the ‘extent of surface exposure’. We show that scores that compare surfaces are useful at low resolutions and for maps with evident surface features. All the scores discussed are implemented in TEMPy (http://tempy.ismb.lon.ac.uk/).

Subfields: Materials Chemistry, Structural Biology, Surfaces, Coatings and Films

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Citations in the corpus

Real-space refinement in PHENIX for cryo-EM and crystallography (2018, background): “…have been developed in crystallography (e.g. Chen et al., 2010; Gore et al., 2012; Young et al., 2017, and references therein) and some in cryo-EM studies (see for example, Henderson et al., 2012; Tickle, 2012; Barad et al., 2015; Pintilie et al., 2016; Joseph et al., 2017, Afonine et al., 2018).”
New tools for the analysis and validation of cryo-EM maps and atomic models (2018, methodology): “The two calculations are: or (Joseph et al., 2017) where ⟨ ⟩ indicates average over all grid points {n}.”
Current approaches for automated model building into cryo-EM maps using Buccaneer with CCP-EM (2020, methodology): “An indication of the overall and local fit of the deposited models to maps are defined by the global cross-correlation coefficient (CCC) and Segment-based Manders’ Overlap Coefficient, respectively, in TEMPy (Farabella et al., 2015; Joseph et al., 2017).”
Integrating model simulation tools and cryo‐electron microscopy (2022, background): “127,174,203 CC evaluates model fit by assessing the similarity between two pixelated volumes.”
Cryo-EM targets in CASP14 (2021, unknown): “The overall goodness-of-fit was quantified using TEMPY’s 2.0 9 cross correlation coefficient (CCC) and Mutual Information (MI) score 10,11 ; PHENIX’s 12 real space correlation coefficients—CCvolume, CCmask, and CCpeaks—each probing different aspects of model-to-map fit 13 ; and the Atom Inclusion…”