2.1.  Image-processing workflow  
The basic elements of the workflow combine classic cryo-EM algorithms with recent improvements in particle picking (Sanchez-Garcia et al., 2018 ▸; Sanchez-Garcia, Segura et al., 2020 ▸; Wagner et al., 2019 ▸) and the key ideas of meta classifiers, which integrate multiple classifiers by a ‘consensus’ approach (Sorzano et al., 2020 ▸), and finish with a totally new approach to map post-processing based on deep learning that we term Deep cryo-EM Map Enhancer (DeepEMhancer; Sanchez-Garcia, Gomez-Blanco et al., 2020 ▸), which complements our previous proposal on local deblurring (Ramírez-Aportela, Vilas et al., 2020 ▸). Naturally, map and map–model quality analyses are performed using a variety of tools (Pintilie et al., 2020 ▸; Ramírez-Aportela, Maluenda et al., 2020 ▸; Vilas et al., 2020 ▸). Conformational variability analysis is carried out by explicitly addressing the continuously flexible nature of the underlying biological reality, in which the SARS-CoV-2 spike explores the conformational space to bind the cellular receptor. Most of the image processing performed in this work was performed using the Scipion framework (de la Rosa-Trevín et al., 2016 ▸), which is a public domain image-processing framework that is freely available at http://scipion.i2pc.es.
A graphical representation of the image-processing workflow used in this work can be found in Supplementary Fig. S1.