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Open data
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Basic information
Entry | Database: PDB / ID: 8fez | |||||||||
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Title | Prefusion-stabilized SARS-CoV-2 spike protein | |||||||||
![]() | Spike glycoprotein![]() | |||||||||
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Function / homology | ![]() Maturation of spike protein / viral translation / Translation of Structural Proteins / Virion Assembly and Release / host cell surface / host extracellular space / suppression by virus of host tetherin activity / Induction of Cell-Cell Fusion / structural constituent of virion / host cell endoplasmic reticulum-Golgi intermediate compartment membrane ...Maturation of spike protein / viral translation / Translation of Structural Proteins / Virion Assembly and Release / host cell surface / host extracellular space / suppression by virus of host tetherin activity / Induction of Cell-Cell Fusion / structural constituent of virion / host cell endoplasmic reticulum-Golgi intermediate compartment membrane / entry receptor-mediated virion attachment to host cell / receptor-mediated endocytosis of virus by host cell / Attachment and Entry / ![]() ![]() ![]() ![]() ![]() Similarity search - Function | |||||||||
Biological species | ![]() ![]() ![]() | |||||||||
Method | ![]() ![]() ![]() | |||||||||
![]() | Gonzalez, K.J. / Mousa, J.J. / Strauch, E.M. | |||||||||
Funding support | ![]()
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![]() | ![]() Title: A general computational design strategy for stabilizing viral class I fusion proteins. Authors: Karen J Gonzalez / Jiachen Huang / Miria F Criado / Avik Banerjee / Stephen Tompkins / Jarrod J Mousa / Eva-Maria Strauch / ![]() Abstract: Many pathogenic viruses, including influenza virus, Ebola virus, coronaviruses, and Pneumoviruses, rely on class I fusion proteins to fuse viral and cellular membranes. To drive the fusion process, ...Many pathogenic viruses, including influenza virus, Ebola virus, coronaviruses, and Pneumoviruses, rely on class I fusion proteins to fuse viral and cellular membranes. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more favorable and stable postfusion state. An increasing amount of evidence exists highlighting that antibodies targeting the prefusion conformation are the most potent. However, many mutations have to be evaluated before identifying prefusion-stabilizing substitutions. We therefore established a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. As a proof of concept, we applied this principle to the fusion protein of the RSV, hMPV, and SARS-CoV-2 viruses. For each protein, we tested less than a handful of designs to identify stable versions. Solved structures of designed proteins from the three different viruses evidenced the atomic accuracy of our approach. Furthermore, the immunological response of the RSV F design compared to a current clinical candidate in a mouse model. While the parallel design of two conformations allows identifying and selectively modifying energetically less optimized positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. We recaptured many approaches previously introduced manually for the stabilization of viral surface proteins, such as cavity-filling, optimization of polar interactions, as well as postfusion-disruptive strategies. Using our approach, it is possible to focus on the most impacting mutations and potentially preserve the immunogen as closely as possible to its native version. The latter is important as sequence re-design can cause perturbations to B and T cell epitopes. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens. | |||||||||
History |
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Structure visualization
Structure viewer | Molecule: ![]() ![]() |
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Downloads & links
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Download
PDBx/mmCIF format | ![]() | 448.4 KB | Display | ![]() |
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PDB format | ![]() | 336.1 KB | Display | ![]() |
PDBx/mmJSON format | ![]() | Tree view | ![]() | |
Others | ![]() |
-Validation report
Arichive directory | ![]() ![]() | HTTPS FTP |
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-Related structure data
Related structure data | ![]() 29035MC ![]() 8e15C M: map data used to model this data C: citing same article ( |
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Similar structure data | Similarity search - Function & homology ![]() |
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Links
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Assembly
Deposited unit | ![]()
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Components
#1: Protein | ![]() Mass: 137991.797 Da / Num. of mol.: 3 Mutation: N856L, A899Q, L916F, Y917W, T941D, A956L, K964E, D985N, P1143Q Source method: isolated from a genetically manipulated source Source: (gene. exp.) ![]() ![]() ![]() Gene: S, 2 / Production host: ![]() ![]() |
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-Experimental details
-Experiment
Experiment | Method: ![]() |
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EM experiment | Aggregation state: PARTICLE / 3D reconstruction method: ![]() |
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Sample preparation
Component | Name: SARS-CoV-2 Spike protein![]() |
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Molecular weight | Experimental value: NO |
Source (natural) | Organism: ![]() ![]() ![]() |
Source (recombinant) | Organism: ![]() ![]() |
Buffer solution | pH: 7.6 |
Specimen | Embedding applied: NO / Shadowing applied: NO / Staining applied![]() ![]() |
Vitrification![]() | Cryogen name: ETHANE |
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Electron microscopy imaging
Experimental equipment | ![]() Model: Titan Krios / Image courtesy: FEI Company |
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Microscopy | Model: TFS KRIOS |
Electron gun | Electron source![]() ![]() |
Electron lens | Mode: BRIGHT FIELD![]() ![]() |
Image recording | Average exposure time: 8 sec. / Electron dose: 58.24 e/Å2 / Film or detector model: GATAN K2 SUMMIT (4k x 4k) |
Image scans | Sampling size: 5 µm / Width: 3710 / Height: 3838 / Movie frames/image: 40 |
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Processing
EM software |
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CTF correction![]() | Type: PHASE FLIPPING AND AMPLITUDE CORRECTION | ||||||||||||||||||||||||
3D reconstruction![]() | Resolution: 3.72 Å / Resolution method: FSC 0.143 CUT-OFF / Num. of particles: 87514 / Symmetry type: POINT | ||||||||||||||||||||||||
Atomic model building | Protocol: AB INITIO MODEL |