Aggregation‐prone peptides from within a non‐self‐protein homoaggregate are preferred for MHC association: Historical overview
Bibliographic record
Abstract
New technologies assist re-evaluation of hypotheses on generation of immune cell repertoires and distinctions of self from non-self. Findings include positive correlations between peptide propensities to aggregate and their binding to major histocompatibility complex (MHC) proteins. This recalls the hypothesis that foreign proteins may homoaggregate in host cytosols prior to releasing their peptides (p) to form pMHC complexes. Clues to this included aggregation-related phenomena associated with infections (rouleaux formation, pyrexia, certain brain diseases). By virtue of 'promiscuous' gene expression by thymic presenting cells - perhaps adapted from earlier evolving gonadal mechanisms - developing T cells monitor surface pMHC clusterings. This evaluates intracellular concentrations of the corresponding proteins, and hence, following Burnet's two signal principle, degrees of self-reactivity. After positive selection in the thymic cortex for reactivity with 'near-self', high-level pMHC clustering suffices in the medulla for negatively selection. Following Burnet's principle, in the periphery low-level clustering suffices for T cell stimulation and high-level clustering again provokes negative selection (immunological tolerance). For evolving intracellular pathogens, fine-tuned polymorphisms of their host species have limited to 'near-self' some mimicking adaptations. It is proposed that while entire pathogen proteins may have evolved to minimize their aggregability, the greater aggregability of their peptides remains partially hidden within. Two-step proofreading mechanisms in prospective hosts select proteins containing aggregable peptide for the generation of pMHC clusters at the surface of presenting cells. Through mutations, some proteins of pathogens and cancer cells tend to converge towards the host 'near-self' that its T cells have auditioned to address.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".