Structural epitope profiling identifies antibodies associated with critical COVID-19 and long COVID
Bibliographic record
Abstract
Abstract Even within a single protein, antibody binding can have beneficial, neutral, or harmful effects during the response to infection. Resolving a polyclonal antibody repertoire across a pathogen’s proteome to specific epitopes may therefore explain much of the heterogeneity in susceptibility to infectious disease. However, the three-dimensional nature of antibody-epitope interactions makes the discovery of non-obvious targets challenging. We implemented a novel computational method and synthetic biology pipeline for identifying epitopes that are functionally important in the SARS-CoV-2 proteome and identified an IgM-dominant response to an exposed Membrane protein epitope which to our knowledge is the strongest correlate of severe disease identified to date (adjusted OR 72.14, 95% CI: 9.71 – 1300.15), stronger even than the exponential association of severe disease with age. We also identify persistence (> 2 years) of this IgM response in individuals with longCOVID, and a correlation with fatigue and depression symptom burden. The repetitive arrangement of this epitope and the pattern of isotype class switching is consistent with this being a previously unrecognized T independent antigen. These findings point to a coronavirus host-pathogen interaction characteristic of severe virus driven immune pathology. This epitope is a promising vaccine and therapeutic target as it is highly conserved through SARS-CoV-2 variant evolution in humans to date and in related coronaviruses (e.g. SARS-CoV), showing far less evolutionary plasticity than targets on the Spike protein. This provides a promising biomarker for longCOVID and a target to complement Spike-directed vaccination which could broaden humoral protection from severe or persistent disease or novel coronavirus spillovers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".