Allostery links hACE2 binding, pan-variant neutralisation and helical extension in the SARS-CoV-2 Spike protein
Bibliographic record
Abstract
Abstract The SARS-CoV-2 spike protein is highly antigenic, with epitopes in three distinct regions of the receptor binding domain (RBD) alone that have known mechanisms of neutralization. In previous work, we predicted a fourth RBD epitope based on allosteric conformational perturbations measured by hydrogen-deuterium exchange mass spectrometry (HDX-MS) upon complexation with the canonical spike protein target, human angiotensin-converting enzyme 2 (hACE2). We subsequently identified a pan-neutralizing antibody (ICO-hu104) with the predicted epitope, however, as the epitope was somewhat distant from the hACE2 binding interface, and our previous work limited to the spike RBD, the neutralization mechanism was unclear. Using HDX-MS, we investigated the binding of ICO-hu104 to the full-length SARS-CoV-2 spike protein from Wuhan, Delta and Omicron variants. We demonstrate that binding of ICO-hu104 at its epitope results in an increase in deuterium uptake in the distant HR1 domain for variants of concern, which in a biological context could be indicative of destabilisation of the helices within this region, promoting S1 shedding or failure of helical extension during S2-mediated fusion. This is supported by our computational modelling, highlighting propagation of allosteric effects to the S2 coiled-coil region. Collectively, this work demonstrates an alternative neutralization mechanism for ICO-hu104 which is distinct from its first-generation predecessors and thus opens alternative avenues targeting non-RBD epitopes through assessment of allosteric perturbations. Highlights HDX-MS reveals decreased deuterium uptake within the HR1 region of S2 for SARS-CoV-2 spike protein for variants of concern when bound to ICO-hu104. Computational modelling validates high allosteric coupling between ICO-hu104 epitope and HR1 region. Suggests an alternative neutralization mechanism from its predecessor ICO-hu23, whereby destabilisation of helices within the HR1 region promotes S1 shedding and/or failure of helical extension during S2-mediated fusion. Graphical abstract
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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 | 0.000 |
| 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".