Comprehensive analysis of SARS-CoV-2 Spike evolution: epitope classification and immune escape prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the COVID-19 pandemic, has produced unprecedented numbers of structures of the Spike protein. In this study, we present a comprehensive analysis of 1560 published structures, covering most major variants that emerged throughout the pandemic, diverse heteromerization, and interacting complexes. Using interaction-energy-informed geometric clustering, we identify 14 structurally distinct epitopes based on their conformational specificity, shared interface with angiotensin-converting enzyme 2 (ACE2), and glycosylation patterns. Our per-residue interaction evaluations accurately predict antibody recognition sites and correlate strongly with deep mutational scanning data, enabling immune escape predictions for future variants. To complement this structural analysis, we integrate longitudinal genomic data from nearly 3 million viral sequences, linking mutational patterns to changes in Spike's conformational dynamics. Our findings reveal two distinct evolutionary trade-offs driving immune escape. First, we confirm an enthalpic trade-off, where mutations in the receptor-binding motif (RBM) enhance immune escape at the cost of weakened ACE2 binding. Second, we introduce an entropic trade-off, showing that mutations outside the RBM modulate Spike's conformational equilibrium, reducing open-state occupancy to evade immune detection-without directly altering the ACE2-binding interface. With these analyses, this work not only highlights the different functional effects of mutations across SARS-CoV-2 Spike variants but also reveals the complex interplay of evolutionary forces shaping the evolution of the SARS-CoV-2 Spike protein over the course of the pandemic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 it