Predicting the feasibility of targeting a conserved region on the S2 domain of the SARS-CoV-2 spike protein
Bibliographic record
Abstract
Abstract The efficacy of vaccines against the SARS-CoV-2 virus significantly declines with the emergence of mutant strains, prompting investigation into the feasibility of targeting highly conserved but often cryptic regions on the S2 domain of spike protein. Using tools from molecular dynamics, we find that this conserved S2 epitope located in the central helices below the receptor binding domains is unlikely to be exposed by dynamic fluctuations without any external facilitating factors, in spite of previous computational evidence suggesting transient exposure of this region. Furthermore, glycans inhibit opening dynamics, and thus stabilize spike in addition to immunologically shielding the protein surface, again in contrast to previous computational findings. Though the S2 epitope region examined here is central to large scale conformational changes during viral entry, free energy landscape analysis obtained using the path coordinate formalism reveals no inherent “loaded spring” effect, suggesting that a vaccine immunogen would tend to present the epitope in a pre-fusion-like conformation and may be effective in neutralization. These findings contribute to a deeper understanding of the dynamic origins of the function of the spike protein, as well as further characterizing the feasibility of the S2 epitope as a therapeutic target.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".