# Focused classifications and refinements in high-resolution single particle cryo-EM analysis

Charles Barchet, Léo Fréchin, Samuel Holvec, Isabelle Hazemann, Ottilie von

Loeffelholz, Bruno P. Klaholz

► To cite this version:

Charles Barchet, Léo Fréchin, Samuel Holvec, Isabelle Hazemann, Ottilie von Loeffelholz, et al. Focused classifications and refinements in high-resolution single particle cryo-EM analysis. Journal of Structural Biology, 2023, 215 (4), pp.108015. 10.1016/j.jsb.2023.108015. hal-04743171

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Distributed under a Creative Commons Attribution 4.0 International License
Focused classifications and refinements in high-resolution single
particle cryo-EM analysis

Charles Barchet<sup>1,2,3,4</sup>, Léo Fréchin<sup>1,2,3,4</sup>, Samuel Holvec<sup>1,2,3,4</sup>, Isabelle Hazemann, Ottilie
von Loeffelholz<sup>1,2,3,4</sup> & Bruno P. Klaholz<sup>1,2,3,4</sup>

<sup>1</sup>Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), 1 rue Laurent Fries, Illkirch, France,

<sup>2</sup>Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France,

<sup>3</sup>Institut National de la Santé et de la Recherche Médicale (Inserm) U964, Illkirch, France,

<sup>4</sup>Université de Strasbourg, Strasbourg, France.

*To whom correspondence should be addressed: klaholz@igbmc.fr

Tel.: 0033.369.48.52.78
Abstract

Recent advances in cryo electron microscopy (cryo-EM) and image processing provide new opportunities to analyse drug targets at high resolution. However, structural heterogeneity limits resolution in many practical cases, hence restricting the level at which structural details can be analysed and drug design be performed. As structural disorder is not spread throughout the entire structure of a given macromolecular complex but instead is found in certain regions that move with respect to others and covering molecular scales from domain conformational changes up to the level of side chain conformations in ligand binding pockets, it is possible to focus the attention on those regions and the associated relative movements. Here we show how the usage of focused classifications and refinements provide insights into global conformational arrangements, exemplified on the human ribosome and on the cannabinoid G protein coupled receptor (GPCR), and how they can improve the local map resolution from an essentially disordered region to the 3-4 Å and finally to the 2 Å resolution range. A systematic analysis with variable spherical masks during focused refinement is presented showing that the choice of an optimal mask size helps refining to high resolution. This study covers several practical approaches on 4 examples illustrating how important mask size & shape and including neighbouring structural elements are for a focused analysis of a macromolecular complex. Such methods will be crucial for cryo-EM structure-based drug design of various medical targets and are applicable to single particle cryo-EM and electron tomography data.
**Introduction**

Structural disorder is a characteristic feature of most macromolecular complexes in biology. This can occur in different ways, e.g. at larger-scale levels when disordered regions or protein domains or entire complexes adopt different conformations, or at the residue level where side-chains may adopt variable or alternative conformations. These arise usually as a function of presence and absence of substrates, ligands or other interactions with proteins or nucleic acids, hence they are functionally very important to understand. However, a mixture of different structural states tends to lead to reduced resolvability of the flexible areas due to the fact that these are averaged into one single 3D structure. Cryo electron microscopy (cryo-EM) analysis in combination with advanced image processing including classification of different structures and refinement of individual regions can significantly improve the interpretability of the cryo-EM maps, provided the individual parts are structurally ordered (cases of complete secondary structure disorder, e.g. of a protein region, can only be addressed if they become ordered, typically upon binding of ligands or other protein interaction partners). To be able to separate different structural (and functional) states of a macromolecular complex and / or to refine a given region or sub-complex it is very useful to identify the region of interest.

For this, approaches in the field include variance analysis using multi-variate statistical analysis (MSA)¹⁻³, K-means clustering⁴⁻⁷ or maximum-likelihood (ML-)⁸⁻¹² based approaches. We and colleagues introduced localized MSA analysis / focused classifications of variable areas identified by variance analysis¹,³,¹³, in practice performed by applying a mask to the area of interest during the MSA analysis (Fig. 1). This then allows sorting different structural states of the overall macromolecular complex with refinement of particle sub-populations²,³,¹⁴,¹⁵ which can be combined with resampling /
bootstrap methods for 3D classifications<sup>2,3,6</sup>, in particular using 3D resampling, jacknifing & bootstrapping. i.e. statistical analysis of random sub-ensembles<sup>3,16–19</sup>. The method of localized analysis<sup>1,3</sup> became then more wide-spread and used in the cryo-EM community to comprise both 2D / 3D classifications<sup>2,3,6,13</sup> and also refinements. Such approaches are now widely used for the analysis of flexible protein domains, known under the term of “focused classification & refinement”<sup>6,20–23</sup> (**Fig. 1**). They can also be applied to (near-) symmetrical complexes to analyse structural heterogeneity among different subunits or virus decorations through “localized reconstructions”<sup>24–26</sup>. Such focused refinements allow different sub-regions of a macromolecular assembly to be refined separately, which allows obtaining improved maps also for the less ordered regions (see **Fig. 1A**). A more automated extension of this are multi-body (MB) refinements, where the refinement of the different parts of a complex is done simultaneously while taking into account their relative orientations within a complex<sup>27,28</sup>. The individually refined regions resulting from focused or MB refinement can afterwards be stitched together to form a composite map.

Ribosomes have been observed to undergo different conformational states during the process of protein synthesis. This involves in particular relative rotational movements of the small and large ribosomal subunits with respect to each other<sup>29,30</sup>, typically corresponding to the two major states of pre- and post-translocation complexes (“ratchet” movement) in which the tRNA progresses from the aminoacyl (A) and peptidyl (P) sites to the P and exit (E) sites respectively, accompanied by a multitude of intermediary states some of which can be stabilized by elongation factors or blocked by antibiotics. Such conformational differences have been first observed on bacterial ribosomes including swivel movements of the head of the small ribosomal subunit, but they also exist in eukaryotic ribosomes in which additional movements such as subunit rolling<sup>31</sup> and more
extensive swivel movements occur. When we determined the cryo-EM structure of the
human ribosome<sup>32</sup> (80S complex including the small and large ribosomal subunits 40S
and 60S, respectively) we also found subunit rolling and several other conformational
changes such as pre-/post-translocation and head & swivel movement of the 40S
ribosomal subunit. The endogenously bound E-site tRNA turned out to be competed out
by an inhibitor, cycloheximide (CHX) that binds in the E-site on the 60S ribosomal
subunit right at the position where the CCA end of the tRNA is normally located<sup>33</sup>. The
human ribosome is a promising anticancer target of eukaryote-specific inhibitors
(originally, some of these are antibiotics targeting the bacterial ribosome)<sup>33</sup>, pointing to
an important link between the possibility of performing the analysis of conformational
changes and drug function, with potential implications of focused refinements for drug
design of various targets in general. In the present case, binding of the CHX inhibitor and
dissociation of the E-site tRNA from the ribosome are processes that do not occur
completely due to a competition between inhibitor and E-site tRNA, but instead give rise
to mixed populations due to an equilibrium between different species that have different
associated conformations. The resulting increased sample heterogeneity can be addressed
by 3D classifications and structure sorting<sup>33</sup>. Further improvement can be obtained by
performing focused refinement of the 40S and 60S ribosomal subunits separately, which
improves particularly the more flexible 40S subunit<sup>21</sup>. For this, rather than using a mask
for the entire 80S, two separate masks for each subunit can be used to refine each part
independently (Fig. 1). This concept can be extended to several regions on a
macromolecular complex and create multiple segmentations, e.g. as the 40S ribosomal
subunit still showed less well defined parts in the 40S head, a focused refinement of the
40S subunit body and head regions was done separately - resulting in a combined focused
refinement with 3 masks, one for the 60S subunit and two for the 40S head and body regions (Fig. 1). Similarly, other protein complexes such as membrane proteins (e.g. Hedgehog receptor, respiratory chain complexes etc.)$^{28,34,35}$ or viruses$^{26,36,37}$ have been analysed using focused refinements and localized reconstructions and sub-particle refinements including after symmetry expansion. Such focused 3D classifications and refinements, also in combination with (partial) subtraction of the densities outside the mask, illustrate the strength of performing multipartite focused refinements to obtain better resolved maps. Yet, extending multipartite approaches more by performing focused refinements on smaller regions often turns out to either not further improve maps or make image processing refinements instable.

In the present study we therefore systematically tested different masks to define optimal settings. In particular, we analysed the effect of performing focused classifications and refinements with differently sized spherical masks or contour-based masks following the shape of the molecule or region of interest (Fig. 1B), exemplified by the analysis of human ribosome complexes including recent high-resolution data$^{38}$ and the cannabinoid G protein coupled receptor (GPCR)$^{39}$, as monitored by comparison of resolution values and detailed features in the obtained cryo-EM maps. Furthermore, we show how in some cases essentially disordered regions become well-resolved after either focused or MB refinement.

## Results & Discussion

As a first example highlighting the benefit of focused refinement, we chose the refinement of a human 80S ribosome complex (see methods) that at an early stage of refinement gave ~4.8 Å resolution in which the main secondary structures (e.g. protein
and RNA helices) are resolved but further details are not visible (Fig. 2). Using separate
masks for the refinement of the 40S and 60S subunits (for example the 40S body, Fig.
2A) substantially improved the cryo-EM map to a level that secondary structure elements
in the more flexible 40S subunit start to be resolved better (average resolution 3.7 Å, Fig.
2C). This is illustrated by a protein loop of ribosomal protein uS5 that is visible only at
low resolution before focused refinement; the refined map exhibits a better continuity
regarding the peptide backbone (Fig. 2B). Additional map filtering, sharpening and
masking (as implemented in the “postprocessing” routine of the RELION software<sup>40</sup>, see
methods) allows visualizing amino acid side-chains that are not visible or partially present
but unclear before focused refinement (Fig. 2B). As the initial image processing was done
with 2x coarsened data, we also checked whether the resolution could be limited by the
pixel size (1.74 Å; gives a Nyquist frequency of 3.48 Å), which was not the case (same
resolution and features in the map when processing with 0.87 Å / pixel sampling). Taken
together, even though nominal resolution values are similar (4.0 versus 3.7 Å) after
postprocessing, the map obtained after focused refinement clearly provides better
backbone connectivity and assignable protein side chains (Fig. 2B).

As a second example, we looked into a high-resolution data set of a human 80S
ribosome complex that was recorded on a new-generation Titan Krios electron
microscope setup (including Selectris X filter and Falcon 4 camera) and that went to 2.5
Å resolution with just 25 000 particles (see methods). To see whether the refinement could
be improved even further we performed focused refinements. Specifically, we asked
whether the mask size could have an influence hypothesizing that an optimal size may
exist with regards to cryo-EM structure refinement stability and map quality. To address
this question, we tested systematically varying mask sizes (Fig. 3). For this, concentric
spherical masks centred on the 60S ribosomal subunit of the 80S ribosome with decreasing radii ranging from 90 Å down to 30 Å were used for independent, parallel refinements in the RELION⁴⁰ software (Fig. 3A).

First, we performed a global refinement (i.e. with a global object-shaped mask of the 80S ribosome) that gave a 3D reconstruction at a resolution of 2.5 Å (Fig. 3B, left panel), which serves as a starting point for focused refinement using differently sized concentric masks. Focusing the refinement on the 60S ribosomal subunit within the 80S ribosome using a spherical mask with a radius of 90 Å (i.e. essentially the entire 60S ribosomal subunit) gave a similar, but slightly improved overall estimated resolution (2.4 Å) compared to the global refinement. However, checking the cryo-EM map quality in this area reveals a slight improvement of the connectivity in the rRNA backbone region and fine details in the map start to improve (Fig. 3B). The detailed features in the cryo-EM map become further improved when using a 60 Å radius mask allowing to reveal fine details on the side-chains (e.g. even the methyl and ethyl moieties of an isoleucine residue become resolved (Fig. 3B). A supplementary decrease of the mask size to 45 Å radius does not further improve the features but instead starts to slightly deteriorate them. Finally, usage of an even smaller mask (30 Å radius) leads to a noticeably noisier map (right panel of Fig. 3B) indicating that there is no benefit in further reducing the mask size. Monitoring the resolution of the 3D reconstructions (on the global 80S ribosome) after focused refinement shows that the 60 Å radius mask indeed provides the best resolution (Fig. 3D; comparisons are done on the postprocessed maps), which is consistent with the improved local resolution values (Fig. 3B) and best local refinement (Fig. 3C). These observations are overall consistent with the refinement parameters of the atomic model that was fitted into the cryo-EM map and refined using real space
refinement in the Phenix$^{41,42}$ software (see methods; e.g. the average B factor is 36-37 Å$^2$ for the 90 and 60 Å radius refinements, while it improves to 30 Å$^2$ for the 45 Å radius but gets worse for the 30 Å sphere refinement; similarly, d$_{99}$ values are best for the 60 Å and 45 Å sphere refinements [2.3 Å resolution] as opposed to 2.5 Å for the 90 Å and 30 Å sphere refinements).

This systematic comparison shows that an optimal mask size exists for focused refinements. For the present data set, a mask size of 60 Å (corresponding to ~26% of the particle box size, 640 pixels at 0.72 Å/pixel) appears optimal, providing best nominal and local resolution values (2.3 and 2.2 Å respectively; resolution is estimated for the region inside the spherical mask) and also best-defined fine details in the refined cryo-EM map, hence providing a significant improvement over a global refinement that would remain at 2.5 Å resolution (Fig. 3B). The reason that a focused refinement performs better than a classical global refinement could be due to an improved refinement of particle alignments and Euler angle assignments during image processing. We therefore monitored Euler angle assignment accuracy at each refinement iteration (Fig. 3E). This analysis shows that the usage of smaller masks (45 and 30 Å radius) creates some refinement instability, while global and large masks perform better. An intermediary-sized mask (60 Å) provides less accuracy in the early refinement iterations but then converges also to stable values (only 0.1° accuracy loss as compared to 0.5 or 0.7° for the calculations with the two smaller masks). The Euler angle accuracy for the 60 Å mask calculation is slightly worse compared to global / 90 Å radius masks (Fig. 3E), which can be expected because a larger amount of structural information present inside a larger mask could help improve the alignments and therefore the Euler angle accuracy (the angle accuracy estimation itself may also be influenced by the non-masked regions outside the mask in the 3D
reconstruction). Nevertheless, the map is better according to the finer features in the map (Fig. 3B), possibly due to the fact that the smaller circular mask comprises less noisy solvent regions around the object as opposed to the large mask, resulting in a reduced noise level which improves 3D reconstructions and corresponding forward projections used for structure refinement within the image processing software. The existence of an optimal mask size (where calculations can be expected to converge faster) points to possible instability of parameter refinements with too small masks because the assignment of particle alignments (translation and rotation) and of Euler angle assignments is done in the RELION software using maximum-likelihood (ML) calculations. Instable ML-based parameter refinement typically leads to increased calculation times because the parameter optimization takes longer to converge or is not converging. This is consistent with the calculation times observed here (15.5 h, 15.3 h, 8h, 6 h and 15.1 h for global refinement and 90 Å, 60 Å, 45 Å and 30 Å radius mask refinements, respectively). A trial with even further reduced mask size (15 Å radius) was not possible to run, probably because of general instability in ML optimization & probability maximisation of parameters⁴³,¹⁰,⁹. Considering that the volume of the spherical masks scales with the 3rd order (the region analysed in 3D varies from ~3 054 000 Å³, ~905 000 Å³, ~382 000 Å³, ~113 000 Å³ for the 90 Å, 60 Å, 45 Å and 30 Å radii masks, respectively), the refined volume varies strongly, which leads to degraded parameter refinement for the smaller masks. The present comparison hence suggests that a focused refinement can reduce calculation times if the settings are well chosen. Instead, suboptimal mask size choices may lead to increased calculation times, probably due to enhanced ML-instability of parameter refinements that converge less well. It is also interesting to note that best results regarding map quality were obtained using solvent
flattening during these refinements to counteract the instability of refinements. For
example, the resolution obtained after refinement for the 60 Å radius mask with solvent
flattening was 2.29 Å compared to 2.93 Å without it (if no postprocessing is applied; after
postprocessing the resolution values become similar: 2.29 Å and 2.30 Å, respectively [no
significant differences seen in the maps], i.e., the effect of solvent flattening during image
processing is reminiscent of the final masking during postprocessing as can be expected).

We then analysed the effect of voxel numbers in more detail: the volume within the
masked regions corresponds to 7.6e⁵, 3.1e⁵, 1.6e⁵, 5.5e⁴ and 7.2e³ Å³, respectively for the
90, 60, 45, 30 and 15 Å masks. Considering the 0.72 Å pixel size, this corresponds to a
2.0e⁶, 8.2e⁵, 4.3e⁵, 1.5e⁵ and 1.9e⁴ voxels for each masked region. A 15 Å mask is too
small to allow a good angle alignment, pointing to a value of minimum amount of voxels
needed. To validate this idea, we performed some further calculations using two-times
coarsened data (1.44 Å pixel size, i.e. with ~8 times less voxels). With these settings the
ML calculations could run through with the first 3 masks but did not with the 30 Å mask,
indicating refinement instability. The corresponding numbers of voxels present in each
of these masks are 2.5e⁵, 1.0e⁵, 5.4e⁴ and 1.8e⁴ respectively for the 90, 60, 45 and 30 Å
masks. Comparison shows that the number of voxels within the 15 Å mask using the
original pixel size are similar to those in the 30 Å mask with two times coarsening (~20
000 voxels). Such low voxel numbers appear not sufficient for performing focused
refinements with spherical masks resulting in instable refinements. This indicates that for
refinement of smaller regions it is advisable to use smaller pixel sizes.

To extend the sphere analysis to other cases we selected the cannabinoid G protein-
coupled receptor (GPCR) complex for which cryo-EM data have been deposited
(EMPIAR 10288)³⁹ and used in the past for image processing tests⁴⁴. It has been selected
also for its relatively small size (compared to the human ribosome) and its multi-domain
nature. We first run a *de novo* structure determination and refinement (see methods),
which included significant cleaning of the data regarding contaminants and artefacts
using 2D classifications. The refinement resulted in a 3.4 Å resolution map overall
comparable to that of the one published originally (Fig. 4; indicated with 3 Å
resolution³⁹). Particularly noisy regions comprise the transmembrane (TM) region due to
the presence of detergents, but also the periphery of the intracellular domains. Next, we
tested whether a spherical refinement of the central region of the GPCR protein could
help. However, this attempt did not improve the structure (~4.5 Å resolution instead of
3.8 Å for the entire protein), probably because only ~10% of active voxels are present
inside the spherical mask due to the multi-domain, non-compact organization of the
protein with solvent and detergent between the protein domains (Fig. 4C). However, our
refinements reveal the interesting presence of an additional domain (Fig. 4B) not visible
in the originally deposited map (EMD-0339). This prompted us to run a structure
prediction for the missing domain (residues 55-181) using Alphafold²⁴⁵, which turns out
to correspond to the GI-alpha-1 subunit of the guanine nucleotide-binding protein (top 1st
homolog is PDB 1CIP), which is highly α-helical. A complete structure prediction of the
intracellular part places this domain next to the Gαᵢ domain, while the newly refined cryo-
EM map localises its position underneath the Gβ domain (Fig. 4B). An uncertain domain
placement by structure prediction methods is not particularly surprising considering the
multidomain nature of the complex, but together with the present cryo-EM analysis its
position could be clarified.

As a further example of masked refinements, we selected a comparison of a cryo-EM
structure (full data set 2, see methods) before and after MB refinement (Fig. 5). This is a
human 80S ribosome refinement in which the three parts of 40S head & body and 60S subunit were refined separately (Fig. 5A) using the MB refinement procedure implemented in the RELION software. While a global refinement would not allow interpreting the 40S subunit head, the MB refinement enables retrieving full density for this region (Fig. 5B). Remarkably, it allows to convert an essentially noisy map that cannot be interpreted into a map with excellent side-chain densities in which all residues can be placed during atomic model building (Fig. 5C). While MB refinements are known to improve maps on other complexes<sup>27,28</sup>, the striking difference seen here before & after MB refinement is probably due to the particularly flexible 40S subunit head in eukaryotic ribosomes including human. It is interesting to note that the nominal average resolution before MB refinement is 2.1 Å, but yet the 40S subunit head would be ill-defined. This underlines the importance of local resolution analysis and detailed map inspection because the average resolution is often not a reliable estimate of map quality.

Taken together, the examples of masked refinements presented here illustrate that refinement strategies for structurally ill-defined subregions of macromolecular complexes are strong tools to consider. They are more complex to implement than standard procedures but without being particularly difficult either, i.e., needing i) selection of the region, ii) creation of masks and iii) additional refinement, including several trials to find an optimal mask size for each specific case, and often increasing calculation times (especially for multibody refinements). Nevertheless, such advanced image processing is particularly promising for the analysis of flexible complexes. In this context, it should be noted that focused refinements in practice work better if at least a partial sorting into the different main structures has been done first, for example by 3D classifications including also focused classifications. Often this is not done because this implies a reduction of the
particle numbers distributed over different 3D classes. Hence, to work best, large data
sets are required for structure sorting and subsequent high-resolution refinement.
However, with the current possibilities of fast data acquisitions using latest-generation
direct electron detectors and including multi-spot & multi-hole strategies this is becoming
more straightforward to do³⁸, hence allowing also better and high-resolution focused
refinements on each structural state.

An additional important aspect to consider is that focused refinements and multi-body
approaches result into separate maps that are put together to create a composite map.
However, composite maps may in some cases not reflect a characteristic conformational
state when the individual regions are refined separately without considering domain
conformations; this is why for depositions of maps in the electron microscopy data base
(EMDB) it is requested to also deposit the overall refined consensus structure even if its
resolution is lower. Such issues can be avoided or at least reduced by first performing 3D
classifications on the entire structure, and then focused refinements of the regions for each
conformational subset of particles.

Our comparative analysis using concentric spherical masks suggests that an optimal
mask size exists for focused refinements. Too small masks tend to lead to less stable
refinements and noisier maps (Fig. 3). Instead, masks need to be relatively large so that
refinements of particle parameters (translational & rotational alignments and Euler
angles) remain stable. This observation also applies to the usage of contour-based masks
following the outlines of a macromolecular complex, including refinements focusing on
a larger substrate or bound protein partner. For drug and inhibitor complexes it could be
tempting to use small masks, e.g. in the 15 Å radius range as this would correspond to the
typical size of an inhibitor-bound ligand-binding pocket. However, from our example
here this is not straightforward to do because larger masks comprising a significant
number of voxels of the object are required to keep the refinements stable, as also
illustrated by our GPCR analysis (Fig. 4). Hence, refinement of ligand-binding pockets
requires to include not only surrounding residues in the first and second shell of
interacting residues that are usually directly relevant for structure-guided drug design, but
instead a larger region is needed that comprises a refinable, sufficiently large part of the
complex including strong secondary structure elements (e.g. protein and RNA helices) of
the drug target as these are the main strong components relevant for precise particle
alignments and Euler angle assignment.

A good pre-alignment is important before starting focused refinements as this
influences their quality (e.g. in this study we did 7-8 initial refinement iterations before
focused refinements, see methods). The proper choice of the mask is also important for
the analysis of larger substrates whose presence can influence the conformations of the
neighbouring structural element. For example, to classify and separate human ribosome
complexes in presence and absence of a tRNA molecule in the exit (E) site one should
use a mask that comprises the tRNA and the neighbouring rRNA elements, in particular
the L1 stalk that changes conformation as a function of presence / absence of the tRNA³³.
In summary, it is worth optimizing the mask size for focused refinements, which helps in
the first place obtaining better cryo-EM map, but also to some extent reduces calculation
times. The latter can be significantly large for focused refinements, but can become very
long for MB refinements – yet they are worth the effort as illustrated by the exciting high-
resolution results presented here (Fig. 5).

MB refinements as implemented in RELION also comprise a principal component
analysis, but this allows only for classification of main movements respective to a
reference body (e.g. 40S ribosomal head with respect to body, or 40S subunit with respect to the 60S subunit) and not for sorting out intrinsic heterogeneity inside a body. Hence, compositional heterogeneity can only be partly addressed in MB refinements because it is limited to relatively small heterogeneity; instead, 3D classifications (MSA- or ML-based) can address large conformational changes better. An additional useful tool is partial signal subtraction of the particles in the region outside the mask<sup>46,47,27,48</sup>, which can further improve 3D classifications, focused refinements and MB refinements. Finally, image processing depends strongly on the image acquisition quality to which various parameters contribute such as sample thickness, usage of energy filters or contrast-enhancing Volta phase plates (VPP); the latter can improve image processing significantly thanks to the improved image contrast, in particular by providing much improved starting values for particle alignments, Euler angle assignments and other refinement parameters<sup>49,50</sup>.

In conclusion, advanced imaging processing such as 2D & 3D classification methods to sort out different conformations and compositions of the structures<sup>1-3,6,8,13,14</sup> followed by focused classifications and refinements<sup>21,26,27</sup> provide promising opportunities for structural analysis by cryo-EM. The present study emphasizes the importance of choosing appropriate masks for focused classifications & refinements and MB refinements, but empirical parameter optimization will often be needed with regards to mask sizes to comprise a sufficient number of voxels for refinement (in turn, this parameter is pixel size-dependent, i.e. for smaller regions to refine well a finer sampling will be required). These will allow extending the level of detailed interpretation in single particle cryo-EM reconstructions but will be also applicable to 3D reconstructions obtained from cryo electron tomography and sub-tomogram averaging. The methods and refinement tools for
localised classifications and refinement used here will be applicable broadly to many
other biological samples, both in single particle cryo-EM and cryo electron tomography
data. In particular, they will help extracting the maximum possible structural information
from cryo-EM images and improve resolution including for the less ordered regions of a
macromolecular complex. This comprehensive comparative analysis of state-of-the art
classification and refinement methods will be crucial for the structural biology field and
make best usage of cryo-EM data to address structure, function and dynamics of various
complexes and improve the interpretation of cryo-EM maps and structural analysis
including of ligand-binding pockets to further extend the promising potential of cryo-EM
for structure-guided drug design.
Acknowledgments

We acknowledge Sasha Ballet for IT support and the platform for integrated structural biology at the CBI. We thank TFS for kindly making their microscope setup available for test data collection. This work was supported by CNRS, Association pour la Recherche sur le Cancer (ARC), Institut National du Cancer (INCa_16099), the Fondation pour la Recherche Médicale (FRM), Ligue nationale contre le cancer (Ligue) and Agence Nationale pour la Recherche (ANR) and USIAS of the University of Strasbourg (USIAS-2018-012). The electron microscope facility was supported by the Alsace Region and by the French Infrastructure for Integrated Structural Biology (FRISBI) ANR-10-INSB-05-01, by the epiRNA grant (Fonds Régional de Coopération, Région Grand Est; Compétitivité Alsace 2014-2020, FEDER), Instruct-ERIC and iNEXT-Discovery.

Correspondence and requests for materials should be addressed to B.P.K.

(klaholz@igbmc.fr). The authors declare no competing financial interests.

Author Contributions

I.H., L.F. & S.H. performed sample preparation, C.B., L.F., S.H. & O.vL. image
processing & structural analysis. All authors analysed the data. B.P.K supervised the
project and wrote the manuscript with input from all authors.
## Material and Methods

### Cryo-EM data collection and processing

#### Focused refinement with shaped mask on human ribosome (example 1)

Human 80S ribosomes were purified from HeLa cells following our established procedures and cryo-EM grids were prepared as previously described<sup>22,32,33,51</sup>. Data for example 1 were collected on the in-house Titan Krios (Thermo Fisher Scientific, MA, USA) equipped with a Cs corrector, a K3 camera, a Quantum GIF energy filter (Gatan Inc., CA, USA) using a total dose of 40 e-/Å<sup>2</sup> fractionated into 40 movie frames over 1.6 s using the SerialEM software<sup>52</sup> (data set 1). The slit width of the energy filter was set to 20 eV. Five movies with a pixel size of 0.862 Å were taken per hole using beam shift with a target defocus range of -0.7 to -2.6 µm. 2735 movies were acquired and pre-processed in Warp<sup>53</sup> including movie alignment taking advantage of the dose-weighting scheme, CTF correction and particle picking.

2D and 3D classifications, 3D refinement & map postprocessing were done using RELION<sup>40</sup> software versions 3.0 and 3.1.3. During particle picking 125 496 particle positions were selected. Particles were extracted coarsened 4 times with a box size of 120 pixels. Particles were classified in 50 2D classes, resulting in 101 524 particles kept for further processing. Particles were re-extracted with a pixel size of 1.72 Å / pixel (2 times coarsened) using a box size of 300 pixels and used as an input for a 3D refinement using as a reference the map of our previous human ribosome structure<sup>22,32</sup> lowpass filtered to 60 Å. The resulting 3.8 Å resolution map was used as reference for a 3D global classification into 4 classes without alignment. 83 582 particles from the main classes were selected and particles were extracted in the original sampling with a box size of 480 pixels followed by a 3D refinement, resulting in a 3.65 Å resolution map. A focused
refinement on the 60S subunit was done using a mask derived from the 3D refinement map with a 15 Å lowpass filter, extended by 7 pixels and with a soft edge of 5 pixels, resulting in a 3.58 Å cryo-EM map. This last refinement output was used as an input for a focused 3D classification without alignment with the help of a mask of the 40S subunit and the exit (E) site tRNA region. A class with 19 595 particles containing a tRNA in their E-site was selected and extracted in the original pixel size of 0.862 Å / pixel with a box size of 480 pixels. They were used as an input for a 3D refinement using our previous human ribosome cryo-EM map²² as a reference low-pass filtered to 60 Å, resulting in a 4.85 Å resolution map. This refinement was then focused-refined from the 8th iteration on (i.e. with pre-refined Euler angles) with a mask on the 40S body derived from the 3D refinement map with a 15 Å lowpass filter, extended by 3 pixels and with a soft edge of 3 pixels (see also our previous work²²), which resulted in a 3.95 Å resolution map. The refinement map and the focused refinement map were then filtered / sharpened and masked (as implemented in the “postprocessing” routine of the RELION software⁴⁰ using a 40S subunit body mask extended by 3 pixels and with a soft edge of 9 pixels with respectively a B factor filtering of -143.94 Å² and -128.25 Å², resulting in two maps of 4.06 Å and 3.68 Å resolution.

## Focused refinement with spherical masks on human ribosome (example 2)

For the data sets of examples 2 and 4, data acquisition was done with the software EPU2 (Thermofisher Scientific) on a Titan Krios G4 electron microscope equipped with an E-CFEG electron source, a Selectris X energy filter and a Falcon 4 camera operating in electron event representation (EER) mode⁵⁴ as described previously³⁸. Data pre-processing was done as described previously³⁸ (data set 2). We then investigated the
influence of mask size on this human ribosome dataset. For this, we prepared circular
masks around the 60S ribosomal subunit with varying radii (30, 45, 60 and 90 Å). Theses
masks have been created by generating several concentric spheres of different radii in
Chimera⁵⁵ using the procedure described in the guidelines published in the cisTEM⁵⁶
website (https://cistem.org/frequently-asked-questions#tab-1-5). The spheres were used
to create masks in RELION using a threshold of 0.001 and a 5 pixels soft-edge.

To test the influence of different masks sizes we used a subset of the data comprising
25 000 randomly selected particles from data set 2 at a sampling of 0.72 Å / pixel and a
box size of 640 pixels (example 2). A global refinement was done in RELION with the
option of ignoring the CTF until the first peak and an initial angular sampling of 7.5
degrees. The refinement converged after 22 iterations resulting in a map with a resolution
of 2.52 Å, which was postprocessed with a global mask of the 80S ribosome extended by
3 pixels and with a soft edge of 9 pixels with a B factor of -14.35 Å². Then, refinement
was continued from the 7th iteration of the previous refinement (hence using
predetermined starting Euler angles) using for focused refinement spherical masks with
radii of 90, 60, 45 and 30 Å and the option of solvent-flattened FSCs. All maps were
postprocessed with the respective same spherical masks that were also used for the
focused refinement, with a B factor of -8.57, -7.14, -13.40 and -17.42 Å² and an estimated
resolution of 2.41, 2.29, 2.45 and 2.49 Å, respectively. A local resolution map for the
global and each focused refinement has been generated using RELION using the B-factor
sharpened map. All calculations with variable circular masks were run on two A100
graphic cards using the same processing parameters. For the estimation of the calculation
time of each job the estimated times for every iteration were summed up (not counting
the extra time between iterations).
## Multibody refinement (example 4)

For example 4, the full data of data set 2 was used. Three tight masks derived from 15 Å lowpass-filtered maps of the ribosomal 60S subunit and the head and body parts of the 40S subunit and extended by 5 pixels with a soft edge of 9 pixels were used for MB refinement. The MB job was run using sigma angles and sigma offset values of 2 in the multibody.star file provided to RELION, and an initial angular sampling of 7.5 degrees. This resulted in 3 separate reconstructions of the three bodies, i.e. a composite map comprising the 60S ribosomal subunit at 2.01 Å resolution, the ribosomal 40S subunit body at 2.13 Å resolution and the 40S head at 2.28 Å resolution.

All resolution estimations by Fourier shell correlation (FSC⁵⁷,⁵⁸) calculations (0.143 criterion) were done in RELION⁴⁰ using half maps. Map interpretation was done by docking the atomic model of the 80S human ribosome (PDB ID 6QZP)²²,⁵⁹ into the final maps followed by an automated rigid body fit in Chimera⁵⁵ and subsequent refinements including manual rebuilding in Coot⁶⁰ and real space refinement in Phenix⁴¹,⁴². Figures were prepared using the softwares ChimeraX 1.2.5⁶¹ and Pymol⁶².

## Focused and spherical refinement on GPCR dataset (example 3)

For the GPCR data processing (example 3), 2753 movies at a pixel size of 0.86 Å / pixel were downloaded from the EMPIAR data bank (EMPIAR 10288)³⁹. The motion correction, CTF estimation and particle picking were performed using cryoSPARC v4.1.2. 2 020 450 particles were selected using the template picker using the EMPIAR 10288 map as a reference³⁹ and imported in RELION 4.0.0 for further processing. 2D classification and 3D classification were performed and 1 424 105 particles were kept for
3D refinement using a tight mask (generated in the same manner as for the ribosome) on the whole GPCR, resulting in a map at 3.8 Å resolution, reaching 3.4 Å after postprocessing. A spherical mask with a 45 Å radius was generated on Chimera and used as an input in RELION to perform a focused refinement. A missing range of amino acids (55-181) of the G-protein alpha subunit as well as the entire G-protein alpha subunit were predicted using AlphaFold2⁴⁵ and then fitted as a rigid body into the cryo-EM map using Chimera.
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## Figure legends

### Figure 1 Concept of local / focused classification and refinement

(a) Schematic representation of the different possibilities of localized analysis on macromolecular complexes in cryo-EM to address sample heterogeneity, separate different conformations and refine sub-regions to obtain high resolution structures by focused classification and refinement procedures. These can include local MSA (multi-variate statistical analysis) / focused 2D & 3D classifications & focused refinements. Top left, initially developed concept of local MSA on class averages and on the particles that they are composed of, focusing on a highly variable region identified by variance analysis¹; this allowed focused classification to sort and separate structures¹; as masks are used either spherical masks or dilated, binarized maps of the region of interest. Next (top middle), extension of the concept towards 3D resampling²,³ & bootstrapping⁶ on particle subpopulations followed by 3D classification (3D resampling & 3D classification, 3D-SC)²,³, and focused classification in 3D⁶,²⁰⁻²³; 3D classifications can be MSA- or ML-(maximum-likelihood) based methods. Applied to the high-resolution structural analysis of the human ribosome (top and right part), 2 or 3 masks for the 60S ribosomal subunits and the more flexible 40S subunit head and body regions were used (figures adapted from²¹,²²; with permission from Elsevier) to generate a highly resolved composite map.

To perform focused classifications and refinements it is often useful to create a mask that includes a slightly larger region than the region of interest, i.e. to include neighbouring structural elements that may move synergistically with a flexible area (identified by variance analysis), or as a function of absence / presence of bound substrates etc. The overall procedure implies to start from a globally aligned dataset that is treated further by
application of binarized masks on the area of interest to sort out heterogeneity and to
improve resolution by focusing refinement calculations on this area only.

(b) Workflows used in the present work. After an initial cryo-EM structure determination and refinement in the context of a global refinement, different approaches can be used: focused classification & refinement using a shaped mask, multibody refinement, or focused refinement using spherical masks (used in the present work for testing the influence of the mask size).

**Figure 2 Example of focused refinement on the 40S body of the human ribosome and the quality of the peptide backbone connectivity**

(a) Overlay of a globally refined 80S map and the 40S body mask that was used for 3D focused refinement and postprocessing of the map (as implemented in the RELION software, see methods; comprises filtering/sharpening & masking).

(b) Map before and after postprocessing for global and focus refinement (the final atomic model is indicated). Density breaks in the peptide backbone are overcome and a good connectivity is obtained. After postprocessing of the map side-chains densities appear, which without focused refinement would not be interpretable nor assignable in the protein sequence.

(c) FSCs curves for global and focused refinement estimated according to the 0.143 criterion (red line).
**Figure 3 Effect of focused refinement using masks with different sphere radii**

(a) Cryo-EM map of the human 80S ribosome after global refinement and the different masks with radii of 90, 60, 45 and 30 Å, respectively, used for the focused refinement on the 60S ribosomal subunit.

(b) Map and atomic model in different regions of the 28S rRNA and ribosomal protein eL32 in the masked area (60S subunit) for the global and each focused refinement. An improvement of the map in backbone, nucleobase and amino acids regions is visibly after the masked focused classification until the 45 Å radius mask where the map starts to deteriorate. Average resolution for the entire complex and local resolution values for the displayed area are indicated.

(c) Sliced view of local resolution maps obtained from global and individual focused refinements. The masked area is indicated by dashed lines. The inset on the right indicates that these are central sections through the cryo-EM map.

(d) FSCs curves for global and focused refinement estimated according to the 0.143 criterion (red line). Best resolution is obtained for the refinement with a 60 Å radius mask, indicated that an optimal mask size exists for focused refinements (color code as for the masks in panel a).

(e) Euler angle accuracy improvement at each iteration throughout the global and the different focused refinements (color code as for the masks in panel a).

**Figure 4 Refinement tests using the EMPIAR 10288 GPCR dataset**

(a) Left: EMPIAR 10288 / EMD 0339 density map. Middle: postprocessed map obtained after image processing using a tight mask on the entire object and local resolution map. Right: local resolution map.
(b) The 45 Å radius sphere used for focus refinement. The resulting refinement (red) is instable due to only 10% of active voxels inside the sphere.

(c) The unsharpened map reveals a supplementary domain of the G-protein alpha subunit. As it is missing in the atomic model (PDB 6n4b), the structure of the corresponding domain (amino acids 55-181) was predicted using AlphaFold2 for the entire subunit (yellow) and the specific fragment (magenta), revealing a different domain positioning in the cryo-EM map (underneath the Gβ domain).

**Figure 5 Improvement of resolution and map features by multibody refinement**

(a) **Left:** Shown are the three refined regions: 60S ribosomal subunit (blue), 40S subunit body (yellow) and 40S head (orange) during the multibody refinement. For each region the rotational and translational movements relative to other regions are estimated. Each region is independently refined during multibody refinement. **Right:** FSC curves for the 3 separate regions after multibody refinement (resolution estimated according to the 0.143 criterion).

(b) Structure of human 80S ribosome before (left) and after (right) multibody refinement focused on the 40S subunit head region, which is essentially invisible following a global refinement but becomes visible and well-defined after multibody refinement.

(c) Details on the structure obtained on the region of the 40S subunit head before (left) and after (right) multibody refinement. The same region is shown with and without the final atomic model (top and bottom, respectively). Multibody refinement is able to align locally the 40S head region so that the densities for proteins and nucleotides become defined and can be interpreted, hence improving the map from an essentially disordered region to the 2 Å resolution range.
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
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<figure><img src="image_5.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3</figcaption></figure>
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<figure><img src="image_8.png" /><figcaption>Figure 5</figcaption></figure>