Modeling Deamidation in Sheep α-Keratin Peptides and Application to Archeological Wool Textiles
Bibliographic record
Abstract
Deamidation of glutamine (Q) and asparagine (N) has been recognized as a marker of degradation and aging in ancient proteins. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to study deamidation in wool textiles, we identified eight peptides from α-keratin proteins in sheep wool that could potentially be used to assess the level of degradation. For each chosen peptide, the extent of deamidation was determined by comparing the calculated theoretical distribution with the measured distribution using a genetic algorithm that gives the best fit to the measured distribution. Variations in the levels of deamidation were observed between peptides and in modern wool samples buried for up to 8 years in which deamidation levels were relatively low under short-term burial. In contrast, deamidation was higher in archeological textile fragments from medieval sites ranging from the 9th to 13th century in York (United Kingdom) and Newcastle (United Kingdom) and from the 13th to 16th century in Reykholt (Iceland). Major differences were observed between the British and the Icelandic samples, showing a negative correlation between age of samples and levels of deamidation, but highlighting the effect of local environment. In addition, nanoscale liquid chromatography-electrospray ionization tandem mass spectrometry (nanoLC-ESI-MS/MS) data indicated that deamidation in wool's α-keratin was influenced by primary and higher-order structures. Predominance of deamidation on glutamine rather than asparagine in the archeological samples was attributed to a higher abundance of Q in the α-helical core domain of keratins, neighboring residues and steric hindrance preventing deamidation of N.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".