Bioinformatics-Based Predictions of Peptide Binding to Disease-Associated HLA Proteins Suggest Explanation for Shared Autoimmunity
Bibliographic record
Abstract
Aim This study was designed to examine the immunogenetic basis for shared autoimmunity, resulting in autoantigen presentation that leads to the production of two or more disease-specific autoantibodies. Methods A bioinformatics approach based on peptide binding predictions to disease-associated HLA determinants has been developed and tested here using 11 disease associations between autoimmune systemic and mucocutaneous blistering disorders. Various HLAs associated with antigens within a given “disease model” (set of HLA class II and protein sequences known to be associated with a specific autoimmune disease) were tested and ranked against the antigenic proteins, first with proteins they are known to associate with and then with proteins known to be implicated in a second disease model. In every case binding predictions were compared for different proteins binding to the same HLA. Subsequently, disease-related autoantigens have been tested for their binding affinity against each disease-specific HLA class II protein. Results For a single HLA haplotype, several binders have been generated from a related autoantigen with the variable binding score. In most cases, the binding score corresponding to the interactions between the autoantigen-derived epitope and the HLA associated with one disease was similar or lower than the interactions between the epitope from proteins associated with the second disease and the same HLA. Notably, there was no compelling promiscuity in peptide binding to each of the HLA molecules, in spite of the promiscuous nature of HLA class II binding. Conclusions The data suggest that, in susceptible individuals, shared autoimmunity might be initiated by two types of HLA/peptide interaction; first between an autoantigen-derived epitope and its disease-associated HLA molecules, and second, between a different peptide of the same autoantigen and HLA proteins specific for the second disease.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".