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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".