PMC:7553147 / 1411-5470 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7553147","sourcedb":"PMC","sourceid":"7553147","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7553147","text":"1. Introduction  \nSARS-CoV-2 infects target cells through the interaction of the viral spike (S) protein with cell receptors. This is an essentially dynamic event that is hard to analyze using most structural biology techniques. However, cryo-EM offers some unique capabilities that makes it a very suitable approach for this task, especially the facts that it can work with noncrystalline samples and, to a certain degree, those with structural flexibility (Dashti et al., 2014 ▸; Maji et al., 2020 ▸; Scheres et al., 2007 ▸; Sorzano et al., 2019 ▸; Tagare et al., 2015 ▸).\nIn turn, cryo-EM information is complex, being buried in thousands of very noisy movies, making it a real challenge to reveal a three-dimensional (3D) structure from this collection of images. Furthermore, cryo-EM is in the middle of a methodo­logical and instrumental ‘revolution’ (Kühlbrandt, 2014 ▸) that has already been in progress for several years, with new methods constantly being produced. In this context, the original data of Wrapp et al. (2020 ▸) have been reanalyzed, applying newer workflows and algorithms, and thus obtaining improved information.\nConsidering that we were studying a biological system that is characterized by its continuous flexibility, we have not strictly followed the standard multi-class approach (Scheres et al., 2007 ▸), which is very well suited to cases of discrete flexibility, since the mathematical modeling and the biological reality could be too far apart. Instead, we have calculated a new ‘ensemble’ map at 3 Å global resolution in which the bias has been carefully reduced, followed by both a 3D classification process and a continuous flexibility analysis in 3D principal component (PC) space using a GPU-accelerated and algorithmically improved version of the method of Tagare et al. (2015 ▸). The ensemble map was used for atomic modeling. Our aim was to explore a larger part of the structural flexibility present in the data set than is achievable by 3D classification alone. Using this mixed procedure, and through scatter plots of the projection of the different particle images onto the principal component axes, we have clearly shown how the spike flexibility in this data set should be understood as a continuum of states rather than discrete conformations. Using maximum-likelihood-based classification, we have obtained two maps that are projected at the extremes of the main principal component on which flexible fitting from the ensemble map has been performed. However, these extreme maps have an intrinsic blurring in the most flexible areas, since for any class that we may define the images come from a continuum of states and are therefore heterogeneous. This flexibility is substantially reduced in a recently described biochemically stabilized spike (Hsieh et al., 2020 ▸), as shown by the reduced blurring, which translates into an improved local resolution.\nIn this work, we describe the new structural information that has been obtained and how it impacts our biological understanding of the system, together with the new workflows and algorithms that have made this accomplishment possible. We used Scipion 2.0 (de la Rosa-Trevín et al., 2016 ▸) in order to easily combine different software suites in the analysis workflows. Maps and models have been deposited in public databases [EMPIAR (Iudin et al., 2016 ▸) and EMDB (Lawson et al., 2011 ▸)]: SARS-CoV-2 spike in the prefusion state as EMDB entry EMD-11328 and PDB entry 6zow, SARS-CoV-2 stabilized spike in the prefusion state (1-up conformation) as EMDB entry EMD-11341, SARS-CoV-2 spike in the prefusion state (flexibility analysis, 1-up closed conformation) as EMDB entry EMD-11336 and PDB entry 6zp5, and SARS-CoV-2 spike in the prefusion state (flexibility analysis, 1-up open conformation) as EMDB entry EMD-11337 and PDB entry 6zp7. All of the used data, the image-processing workflow and the intermediate results were also uploaded to EMPIAR (entries EMPIAR-10514 and EMPIAR-10516) by running the EMPIAR automatic deposition feature in 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