|Title||Continuous flexibility analysis of SARS-CoV-2 Spike prefusion structures.|
|Journal, issue, pages||bioRxiv, Year 2020|
|Publish date||Jul 8, 2020|
|Authors||Roberto Melero / Carlos Oscar S Sorzano / Brent Foster / José-Luis Vilas / Marta Martínez / Roberto Marabini / Erney Ramírez-Aportela / Ruben Sanchez-Garcia / David Herreros / Laura Del Caño / Patricia Losana / Yunior C Fonseca-Reyna / Pablo Conesa / Daniel Wrapp / Pablo Chacon / Jason S McLellan / Hemant D Tagare / Jose-Maria Carazo /|
|PubMed Abstract||With the help of novel processing workflows and algorithms, we have obtained a better understanding of the flexibility and conformational dynamics of the SARS-CoV-2 spike in the prefusion state. We ...With the help of novel processing workflows and algorithms, we have obtained a better understanding of the flexibility and conformational dynamics of the SARS-CoV-2 spike in the prefusion state. We have re-analyzed previous cryo-EM data combining 3D clustering approaches with ways to explore a continuous flexibility space based on 3D Principal Component Analysis. These advanced analyses revealed a concerted motion involving the receptor-binding domain (RBD), N-terminal domain (NTD), and subdomain 1 and 2 (SD1 & SD2) around the previously characterized 1-RBD-up state, which have been modeled as elastic deformations. We show that in this dataset there are not well-defined, stable, spike conformations, but virtually a continuum of states moving in a concerted fashion. We obtained an improved resolution ensemble map with minimum bias, from which we model by flexible fitting the extremes of the change along the direction of maximal variance. Moreover, a high-resolution structure of a recently described biochemically stabilized form of the spike is shown to greatly reduce the dynamics observed for the wild-type spike. Our results provide new detailed avenues to potentially restrain the spike dynamics for structure-based drug and vaccine design and at the same time give a warning of the potential image processing classification instability of these complicated datasets, having a direct impact on the interpretability of the results.|
|External links||bioRxiv / PubMed:32676604 / PubMed Central|
|Methods||EM (single particle)|
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