Real-time Conformational Dynamics of SARS-CoV-2 Spikes on Virus Particles
Bibliographic record
Abstract
SARS-CoV-2 spike (S) mediates entry into cells and is critical for vaccine development against COVID-19. S is synthesized as a precursor, processed into S1 and S2 by furin proteases, and activated for fusion when human angiotensin-converting enzyme 2 (hACE2) engages the receptor-binding domain (RBD) and when the N-terminus of S2 is proteolytically processed. Structures of soluble ectodomains and native virus particles have revealed distinct conformations of S, including a closed trimer with all RBD oriented downward, trimers with one or two RBDs up, and hACE2-stabilized conformations with up to three RBD oriented up. Real-time information that connects these structures, however, has been lacking. Here we apply single-molecule Forster Resonance Energy Transfer (smFRET) imaging to observe conformational dynamics of S on virus particles. Virus-associated S dynamically samples at least four distinct conformational states. In response to hACE2, S opens into the hACE2-bound S conformation through at least one on-path intermediate, with trypsin partially activating S. Conformational preferences of convalescent patient plasma and monoclonal antibodies suggest mechanisms of neutralization involving either direct competition with hACE2 for binding to RBD or allosteric interference with conformational changes required for entry. Our findings inform on mechanisms of S recognition and on conformations for immunogen design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".