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LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T2","span":{"begin":2281,"end":2289},"obj":"Body_part"},{"id":"T3","span":{"begin":2397,"end":2406},"obj":"Body_part"},{"id":"T4","span":{"begin":5003,"end":5008},"obj":"Body_part"},{"id":"T5","span":{"begin":5104,"end":5109},"obj":"Body_part"},{"id":"T6","span":{"begin":5673,"end":5677},"obj":"Body_part"},{"id":"T7","span":{"begin":5734,"end":5739},"obj":"Body_part"},{"id":"T8","span":{"begin":6541,"end":6546},"obj":"Body_part"},{"id":"T9","span":{"begin":7160,"end":7165},"obj":"Body_part"}],"attributes":[{"id":"A2","pred":"uberon_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/UBERON_0004288"},{"id":"A3","pred":"uberon_id","subj":"T3","obj":"http://purl.obolibrary.org/obo/UBERON_2000106"},{"id":"A4","pred":"uberon_id","subj":"T4","obj":"http://purl.obolibrary.org/obo/UBERON_0001463"},{"id":"A5","pred":"uberon_id","subj":"T5","obj":"http://purl.obolibrary.org/obo/UBERON_0001463"},{"id":"A6","pred":"uberon_id","subj":"T6","obj":"http://purl.obolibrary.org/obo/UBERON_0002398"},{"id":"A7","pred":"uberon_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/UBERON_0002542"},{"id":"A8","pred":"uberon_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/UBERON_0001463"},{"id":"A9","pred":"uberon_id","subj":"T9","obj":"http://purl.obolibrary.org/obo/UBERON_0001463"}],"text":"3. Results \nWith the goal set at analyzing spike flexibility, we describe our key results step by step.\n\n3.1. The ensemble map and the way to obtain it \nIn the following, we describe the analysis of SARS-CoV-2 spike stabilized in the prefusion state by two proline substitutions in S2 (S-2P). We will objectively demonstrate that the flexibility of the spike protein should be understood as a quasi-continuum of conformations, so that when performing a structural analysis on this specimen special care has to be paid to the image-processing workflows, since they may directly impact on the interpretability of the results.\nStarting from the original SARS-CoV-2 S-2P data set of Wrapp et al. (2020 ▸), we have completely reanalyzed the data using our public domain software integration platform Scipion (de la Rosa-Trevín et al., 2016 ▸), breaking the global 3 Å resolution barrier. A representative view of the new ensemble map and its corresponding global FSC curve is shown in Fig. 1 ▸(a) (EMDB entry EMD-11328); the sequence of a monomer of the S protein is shown on the right to facilitate the further discussion of structure–function relationships (from Wrapp et al., 2020 ▸). Figs. 1 ▸(b) and 1 ▸(c) show a comparison between the original map (Wrapp et al., 2020 ▸) with EMDB code EMD-21375 and the newly reconstructed ensemble map corresponding to EMD-11328. Clearly, the local resolution (Vilas et al., 2018 ▸), which is shown on the left in Figs. 1 ▸(b) and 1 ▸(c), is increased in the new map, and the anisotropy, which is shown in the center, is much reduced. Finally, on the right we present plots of the radially averaged tangential resolution, which is related to the quality of the angular alignment (Vilas et al., 2020 ▸); the steeper the slope, the higher the angular assignment error. As can be appreciated, the slope calculated from the newly obtained map is almost zero when compared with that for the map from Wrapp et al. (2020 ▸), indicating that, in relative terms, the particle alignment used to create the new map is better than that used to build the original map. The result is an overall quantitative enhancement in map quality.\nIn terms of tracing, besides modeling several additional residue side chains and improving the geometry of the carbon skeleton (see Supplementary Fig. S2), one of the most noticeable improvements that we observed in the new map is an extension of the glycan chains that were initially built, particularly throughout the S2 fusion subunit (PDB entry 6zow). A quantitative comparison can be made between the length of glycan chains in the new ‘ensemble structure’ with respect to the previous structure (PDB entry 6vsb; see Supplementary Table S2). Although the total number of N-linked glycosylation sequons throughout the SARS-CoV-2 S trimer is essentially the same in the new structure (45) and PDB entry 6vsb (44), we have substantially increased the length of the glycan chains, expanding the total number of glycans by about 50%. We note the importance of this extensive glycosylation for epitope accessibility and how the accurate determination of this glycan shield will facilitate efforts to rapidly develop effective vaccines and therapeutics. Supplementary Fig. S2 shows a representative section of sharpened versions of the ensemble map (EMDB entry EMD-11328) compared with EMDB entry EMD-21375, in which the glycans can now be better traced. However, we should not forget that the ensemble map contains images in which the receptor-binding domain (RBD) and N-terminal domain (NTD) are in different positions (see Section 3.2), and consequently these domains appear to be blurred. Details of how the tracing was performed can be found in Section 2, while in Supplementary Fig. S3 we present two map-to-model quality figures indicating the good fit in general, with the obvious exception of the variable parts.\n\n3.2. Flexibility analysis \nStarting from a carefully selected set of particles obtained from our consensus and cleaning approaches (see Section 2), together with the ensemble map described previously, we subjected the data to the following flexibility analysis.\nThe original images that were part of the ensemble map went through a ‘consensus classification’ procedure aimed at separating them into two algorithmically stable classes. Essentially, and as described in more detail in Section 2, we performed two independent classifications, further selecting those particles that were consistently together throughout the two classifications. In this way, we obtained the two new classes shown in Fig. 2 ▸(a). We will refer to these as the ‘closed conformation’ [Fig. 2 ▸(a), Class 1, EMDB entry EMD-11336] and the ‘open conformation’ [Fig. 2 ▸(a), Class 2, EMDB entry EMD-11337]. The number of images in each class was reduced to 45 000 in one case and 21 000 in the other, with global FSC-based resolutions of 3.1 and 3.3 Å, respectively.\nThe open and closed structures depict a clear and concerted movement of the ‘thumb’ formed by the RBD and subdomains 1 and 2 (SD1 and SD2) and the NTD of an adjacent chain. The thumb moves away from the central spike axis, exposing the RBD in the up conformation. In order to make clearer where the changes are at the level of the Class 1 and Class 2 maps, we have made use of the representation of map local strains in Sorzano et al. (2016 ▸), which helps to very clearly visualize the type of strains needed to relate two maps, whether these are rigid-body rotations or more complex deformations (stretching). We have termed the maps resulting from this elastic analysis as ‘1s’ (Class 1, stretching) and ‘1r’ (Class 1, rotations) on the right-hand side of Fig. 2 ▸(a) and the same for Class 2. The color scale for both stretching and rotations goes from blue for small to red for large. Clearly, the differences among the classes with respect to the NTD and RBD have a very substantial component of pure coordinated rigid-body rotations, while the different RBDs present a much more complex pattern of deformations (stretching), indicating an important structural rearrangement in this area that does not occur elsewhere in the specimen. In terms of atomic modeling, we performed a flexible fitting of the ensemble model onto the closed and open forms [see Fig. 2 ▸(a), rightmost map; the PDB code for the open conformation is PDB entry 6zp7, while that for the closed conformation is 6zp5]. Focusing on rotations, which are the most simple element to follow, we can quantify that the degree of rotation of the thumb in these classes is close to 6°, as shown in Fig. 2 ▸(b). Given this flexibility, we consider that the best way to correctly present the experimental results is through the movie provided as Supplementary Movie S1, in which maps and atomic models are presented. Within the approximation to modeling that a flexible fitting represents, we can appreciate two hinge movements of the RBD–SD1–SD2 domains: one located between amino acids 318–326 and 588–595 that produces most of the displacement, and the other between amino acids 330–335 and 527–531 that accompanies a less pronounced ‘up’ movement of the RBD. This thumb motion is completed by the accompanying motion of the NTD from an adjacent chain. Also in a collective way, other NTDs and RBDs in the down conformation move slightly, as can better be appreciated in Supplementary Movie S1, where the transition between fitted models overlaps with the interpolation between observed high-resolution class maps.\nTo further investigate whether or not the flexibility was continuous, we proceeded as follows. Images from the two classes were pooled together and, using the ensemble map, subjected to a 3D principal component analysis (PCA). The approach we followed is based on Tagare et al. (2015 ▸), with some minor modifications of the method. A detailed explanation of the modifications is given in Section 2. We initialized the first principal component (PC) to the difference between the open and closed conformation, while the remaining PCs were initialized randomly. Upon convergence, the eigenvalue of each PC and the scatter of the images in PC space was calculated. The eigenvalues of the PCs are shown in Fig. 3 ▸(a). Clearly, the first three PCs are significant. The scatter plot of the image data in PC1–PC3 space is shown in Fig. 3 ▸(b). Fig. 3 ▸(b) strongly suggests that there is ‘continuous flexibility’ rather than ‘tightly clustered’ flexibility. Fig. 3 ▸(b) also shows the projection of the maps corresponding to the open and closed conformations on the extremes of the first three PCs. It is clear that the open and closed conformations are aligned mostly along the first PC, suggesting that the open/closed classification captures the most significant changes. Fig. 3 ▸(c) shows side views of a pair of structures (mean ± 2 × std, where std is the square root of the eigenvalue) for each PC. Additional details of these structures are available in Supplementary Figs. S4 and S5. Note that PCs are not to be understood as structural pathways with a biological meaning, but as directions that summarize the variance of a data set. For instance, the fact that the RBD appears and disappears at the two extremes of PC3 indicates that there is an important variability in these voxels, which is probably indicative of the up and down conformations of the RBD [to be understood in the context of the elastic analysis shown in Fig. 2 ▸(b)].\nThrough this combination of approaches, we have learnt that the spike conformation fluctuates virtually randomly in a rather continuous manner. Additionally, the approach taken to define the two algorithmically stable ‘classes’ has clearly partitioned the data set according to the main axis of variance, PC1, since the projections of the maps of these classes fall almost exclusively along PC1 and are located towards the extremes of the image-projection cloud. Note that the fraction of structural flexibility owing to PC2 and PC3 is also important in terms of the total variance of the complete image set, but that classification approaches do not seem to properly explore it. Unfortunately, the resolution in PC2 and PC3 is currently limited, so it is difficult to derive clear structural conclusions from these low-resolution maps. However, it is clear from these data that the dynamics of the spike are far richer than just a rigid body closing and opening, and involves more profound rearrangements, especially at the RBD but also at other sites. This observation is similar to that of Ke et al. (2020 ▸) when working with subtomogram averaging.\nAdditionally, the fact that PCA indicates this continuous flexibility to be a key characteristic of the spike dynamics also suggests that many other forms of partitioning (rather than properly ‘classifying’) of this continuous data set could be devised, this fact just being a consequence of the intrinsic instability created by forcing a quasi-continuous data distribution without any clustering structure to fit into a defined set of clusters. In this work, we have clearly forced the classification to go to the extremes of the data distribution, as shown in Fig. 3 ▸, probably by enforcing an algorithmically stable classification, but the key result is that any other degree of movement of the spike in between these extremes of PC1 as well as PC2 and PC3 would also be consistent with the experimental data. In other words, since the continuum of conformations does not have clear ‘cutting/classification’ points, there is a certain algorithmic uncertainty and instability as to the possible results of a classification process. Note that this instability could be exacerbated by the step of particle picking, in the sense that different picking algorithms may have different biases (precisely to minimize this instability, we have performed a ‘consensus’ approach to picking throughout this work).\nClearly, flexibility is key in this system, so that alterations in its dynamics may cause profound effects, including viral neutralization, and this could be one of the reasons for the neutralization mechanism of antibodies directed against the NTD (Chi et al., 2020 ▸).\n\n3.3. Structure of a biochemically stabilized form of the spike \nWe have also worked with a more recent variant containing six proline substitutions in S2 (HexaPro). This second protein was also studied by Hsieh et al. (2020 ▸). In this case, after going through the same stringent particle-selection process as for the previous specimen, as presented in depth in Section 2, it was impossible to obtain stable classes, so that in Fig. 4 ▸ we present a single map (EMDB entry EMD-11341) together with its global FSC curve and a local resolution analysis. It is clear that the local resolution has increased in the moving parts (mostly the RBD and NTD), although we did not feel confident in further modeling."}

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T5","span":{"begin":2739,"end":2761},"obj":"http://purl.obolibrary.org/obo/GO_0006487"},{"id":"T6","span":{"begin":2748,"end":2761},"obj":"http://purl.obolibrary.org/obo/GO_0070085"},{"id":"T7","span":{"begin":3038,"end":3051},"obj":"http://purl.obolibrary.org/obo/GO_0070085"}],"text":"3. Results \nWith the goal set at analyzing spike flexibility, we describe our key results step by step.\n\n3.1. The ensemble map and the way to obtain it \nIn the following, we describe the analysis of SARS-CoV-2 spike stabilized in the prefusion state by two proline substitutions in S2 (S-2P). We will objectively demonstrate that the flexibility of the spike protein should be understood as a quasi-continuum of conformations, so that when performing a structural analysis on this specimen special care has to be paid to the image-processing workflows, since they may directly impact on the interpretability of the results.\nStarting from the original SARS-CoV-2 S-2P data set of Wrapp et al. (2020 ▸), we have completely reanalyzed the data using our public domain software integration platform Scipion (de la Rosa-Trevín et al., 2016 ▸), breaking the global 3 Å resolution barrier. A representative view of the new ensemble map and its corresponding global FSC curve is shown in Fig. 1 ▸(a) (EMDB entry EMD-11328); the sequence of a monomer of the S protein is shown on the right to facilitate the further discussion of structure–function relationships (from Wrapp et al., 2020 ▸). Figs. 1 ▸(b) and 1 ▸(c) show a comparison between the original map (Wrapp et al., 2020 ▸) with EMDB code EMD-21375 and the newly reconstructed ensemble map corresponding to EMD-11328. Clearly, the local resolution (Vilas et al., 2018 ▸), which is shown on the left in Figs. 1 ▸(b) and 1 ▸(c), is increased in the new map, and the anisotropy, which is shown in the center, is much reduced. Finally, on the right we present plots of the radially averaged tangential resolution, which is related to the quality of the angular alignment (Vilas et al., 2020 ▸); the steeper the slope, the higher the angular assignment error. As can be appreciated, the slope calculated from the newly obtained map is almost zero when compared with that for the map from Wrapp et al. (2020 ▸), indicating that, in relative terms, the particle alignment used to create the new map is better than that used to build the original map. The result is an overall quantitative enhancement in map quality.\nIn terms of tracing, besides modeling several additional residue side chains and improving the geometry of the carbon skeleton (see Supplementary Fig. S2), one of the most noticeable improvements that we observed in the new map is an extension of the glycan chains that were initially built, particularly throughout the S2 fusion subunit (PDB entry 6zow). A quantitative comparison can be made between the length of glycan chains in the new ‘ensemble structure’ with respect to the previous structure (PDB entry 6vsb; see Supplementary Table S2). Although the total number of N-linked glycosylation sequons throughout the SARS-CoV-2 S trimer is essentially the same in the new structure (45) and PDB entry 6vsb (44), we have substantially increased the length of the glycan chains, expanding the total number of glycans by about 50%. We note the importance of this extensive glycosylation for epitope accessibility and how the accurate determination of this glycan shield will facilitate efforts to rapidly develop effective vaccines and therapeutics. Supplementary Fig. S2 shows a representative section of sharpened versions of the ensemble map (EMDB entry EMD-11328) compared with EMDB entry EMD-21375, in which the glycans can now be better traced. However, we should not forget that the ensemble map contains images in which the receptor-binding domain (RBD) and N-terminal domain (NTD) are in different positions (see Section 3.2), and consequently these domains appear to be blurred. Details of how the tracing was performed can be found in Section 2, while in Supplementary Fig. S3 we present two map-to-model quality figures indicating the good fit in general, with the obvious exception of the variable parts.\n\n3.2. Flexibility analysis \nStarting from a carefully selected set of particles obtained from our consensus and cleaning approaches (see Section 2), together with the ensemble map described previously, we subjected the data to the following flexibility analysis.\nThe original images that were part of the ensemble map went through a ‘consensus classification’ procedure aimed at separating them into two algorithmically stable classes. Essentially, and as described in more detail in Section 2, we performed two independent classifications, further selecting those particles that were consistently together throughout the two classifications. In this way, we obtained the two new classes shown in Fig. 2 ▸(a). We will refer to these as the ‘closed conformation’ [Fig. 2 ▸(a), Class 1, EMDB entry EMD-11336] and the ‘open conformation’ [Fig. 2 ▸(a), Class 2, EMDB entry EMD-11337]. The number of images in each class was reduced to 45 000 in one case and 21 000 in the other, with global FSC-based resolutions of 3.1 and 3.3 Å, respectively.\nThe open and closed structures depict a clear and concerted movement of the ‘thumb’ formed by the RBD and subdomains 1 and 2 (SD1 and SD2) and the NTD of an adjacent chain. The thumb moves away from the central spike axis, exposing the RBD in the up conformation. In order to make clearer where the changes are at the level of the Class 1 and Class 2 maps, we have made use of the representation of map local strains in Sorzano et al. (2016 ▸), which helps to very clearly visualize the type of strains needed to relate two maps, whether these are rigid-body rotations or more complex deformations (stretching). We have termed the maps resulting from this elastic analysis as ‘1s’ (Class 1, stretching) and ‘1r’ (Class 1, rotations) on the right-hand side of Fig. 2 ▸(a) and the same for Class 2. The color scale for both stretching and rotations goes from blue for small to red for large. Clearly, the differences among the classes with respect to the NTD and RBD have a very substantial component of pure coordinated rigid-body rotations, while the different RBDs present a much more complex pattern of deformations (stretching), indicating an important structural rearrangement in this area that does not occur elsewhere in the specimen. In terms of atomic modeling, we performed a flexible fitting of the ensemble model onto the closed and open forms [see Fig. 2 ▸(a), rightmost map; the PDB code for the open conformation is PDB entry 6zp7, while that for the closed conformation is 6zp5]. Focusing on rotations, which are the most simple element to follow, we can quantify that the degree of rotation of the thumb in these classes is close to 6°, as shown in Fig. 2 ▸(b). Given this flexibility, we consider that the best way to correctly present the experimental results is through the movie provided as Supplementary Movie S1, in which maps and atomic models are presented. Within the approximation to modeling that a flexible fitting represents, we can appreciate two hinge movements of the RBD–SD1–SD2 domains: one located between amino acids 318–326 and 588–595 that produces most of the displacement, and the other between amino acids 330–335 and 527–531 that accompanies a less pronounced ‘up’ movement of the RBD. This thumb motion is completed by the accompanying motion of the NTD from an adjacent chain. Also in a collective way, other NTDs and RBDs in the down conformation move slightly, as can better be appreciated in Supplementary Movie S1, where the transition between fitted models overlaps with the interpolation between observed high-resolution class maps.\nTo further investigate whether or not the flexibility was continuous, we proceeded as follows. Images from the two classes were pooled together and, using the ensemble map, subjected to a 3D principal component analysis (PCA). The approach we followed is based on Tagare et al. (2015 ▸), with some minor modifications of the method. A detailed explanation of the modifications is given in Section 2. We initialized the first principal component (PC) to the difference between the open and closed conformation, while the remaining PCs were initialized randomly. Upon convergence, the eigenvalue of each PC and the scatter of the images in PC space was calculated. The eigenvalues of the PCs are shown in Fig. 3 ▸(a). Clearly, the first three PCs are significant. The scatter plot of the image data in PC1–PC3 space is shown in Fig. 3 ▸(b). Fig. 3 ▸(b) strongly suggests that there is ‘continuous flexibility’ rather than ‘tightly clustered’ flexibility. Fig. 3 ▸(b) also shows the projection of the maps corresponding to the open and closed conformations on the extremes of the first three PCs. It is clear that the open and closed conformations are aligned mostly along the first PC, suggesting that the open/closed classification captures the most significant changes. Fig. 3 ▸(c) shows side views of a pair of structures (mean ± 2 × std, where std is the square root of the eigenvalue) for each PC. Additional details of these structures are available in Supplementary Figs. S4 and S5. Note that PCs are not to be understood as structural pathways with a biological meaning, but as directions that summarize the variance of a data set. For instance, the fact that the RBD appears and disappears at the two extremes of PC3 indicates that there is an important variability in these voxels, which is probably indicative of the up and down conformations of the RBD [to be understood in the context of the elastic analysis shown in Fig. 2 ▸(b)].\nThrough this combination of approaches, we have learnt that the spike conformation fluctuates virtually randomly in a rather continuous manner. Additionally, the approach taken to define the two algorithmically stable ‘classes’ has clearly partitioned the data set according to the main axis of variance, PC1, since the projections of the maps of these classes fall almost exclusively along PC1 and are located towards the extremes of the image-projection cloud. Note that the fraction of structural flexibility owing to PC2 and PC3 is also important in terms of the total variance of the complete image set, but that classification approaches do not seem to properly explore it. Unfortunately, the resolution in PC2 and PC3 is currently limited, so it is difficult to derive clear structural conclusions from these low-resolution maps. However, it is clear from these data that the dynamics of the spike are far richer than just a rigid body closing and opening, and involves more profound rearrangements, especially at the RBD but also at other sites. This observation is similar to that of Ke et al. (2020 ▸) when working with subtomogram averaging.\nAdditionally, the fact that PCA indicates this continuous flexibility to be a key characteristic of the spike dynamics also suggests that many other forms of partitioning (rather than properly ‘classifying’) of this continuous data set could be devised, this fact just being a consequence of the intrinsic instability created by forcing a quasi-continuous data distribution without any clustering structure to fit into a defined set of clusters. In this work, we have clearly forced the classification to go to the extremes of the data distribution, as shown in Fig. 3 ▸, probably by enforcing an algorithmically stable classification, but the key result is that any other degree of movement of the spike in between these extremes of PC1 as well as PC2 and PC3 would also be consistent with the experimental data. In other words, since the continuum of conformations does not have clear ‘cutting/classification’ points, there is a certain algorithmic uncertainty and instability as to the possible results of a classification process. Note that this instability could be exacerbated by the step of particle picking, in the sense that different picking algorithms may have different biases (precisely to minimize this instability, we have performed a ‘consensus’ approach to picking throughout this work).\nClearly, flexibility is key in this system, so that alterations in its dynamics may cause profound effects, including viral neutralization, and this could be one of the reasons for the neutralization mechanism of antibodies directed against the NTD (Chi et al., 2020 ▸).\n\n3.3. Structure of a biochemically stabilized form of the spike \nWe have also worked with a more recent variant containing six proline substitutions in S2 (HexaPro). This second protein was also studied by Hsieh et al. (2020 ▸). In this case, after going through the same stringent particle-selection process as for the previous specimen, as presented in depth in Section 2, it was impossible to obtain stable classes, so that in Fig. 4 ▸ we present a single map (EMDB entry EMD-11341) together with its global FSC curve and a local resolution analysis. It is clear that the local resolution has increased in the moving parts (mostly the RBD and NTD), although we did not feel confident in further modeling."}

LitCovid-sentences

LitCovid-PubTator