Use of anti-neoepitope antibodies for the analysis of degradative events in cartilage and the molecular basis for neoepitope specificity
Bibliographic record
Abstract
Degradation of the cartilage proteoglycan, aggrecan, is an essential aspect of normal growth and development, and of joint pathology. The roles of different proteolytic enzymes in this process can be determined from the sites of cleavage in the aggrecan core protein, which generates novel termini (neoepitopes). Antibodies specific for the different neoepitopes generated by such cleavage events provide powerful tools with which to analyse these processes. The same approach can be used to differentiate the processed, active forms of proteases from their inactive pro-forms. Since the proteolytic processing of these enzymes requires the removal of the inhibitory pro-region, it also results in the generation of N-terminal neoepitopes. Using the newborn rat long bone as a model system, it was shown that the active form of ADAMTS-4 [ADAM (a disintegrin and metalloproteinase) with thrombospondin motifs-4], but not ADAMTS-5, co-localizes with the aggrecan cleavage neoepitopes known to be produced by this metalloproteinase. Thus, in long bone growth, aggrecan turnover seems to be dependent on ADAMTS-4 activity. To demonstrate the molecular basis of the specificity of anti-neoepitope antibodies, the Fv region of a monoclonal antibody specific for a neoepitope generated by the ADAMTS-4-mediated cleavage of aggrecan has been modelled and the binding of the peptide epitope simulated. In the docked structure, the N-terminus of the peptide antigen is clearly buried in the binding-site cavity. The absence of an open cleft makes it impossible for the intact substrate to pass through the binding site, providing a rationale for the specificity of this class of antibodies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".