Pepsin Digestion for Proteomic Studies of the Human Hair Shaft
Why this work is in the frame
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Bibliographic record
Abstract
RATIONALE: Human hair shafts have received increased research interest owing to their easy accessibility and potential as a window for human health. Because the most abundant component in hair is protein, proteomics is a promising tool for studying the molecular composition of hair shafts. As one of the most sophisticated biomaterials, hair shafts also possess unique structures, particularly keratin intermediate filaments, posing challenges for proteomic sample processing. Previously, we discovered that incomplete trypsin proteolysis increased keratin sequence coverage but resulted in an abnormal stoichiometry between types I and II cuticular keratins (PMCID: 12130615). METHODS: In the present study, we explored the potential to re-examine the human hair proteome through pepsin proteolysis and evaluate whether the previously observed type II to type I keratin ratio was due to enzyme biases introduced particularly by trypsin digestion. RESULTS: After optimizing the pepsin digestion conditions, we not only confirmed that previous bias was indeed contributed by trypsin but also discovered that pepsin was more effective at identifying keratin-associated proteins, another main protein component than keratins in human hair shafts. CONCLUSIONS: Spectral counting on trypsin-based proteomics has been widely used to study the stoichiometry of protein complexes. For the first time, we confirmed a large bias caused by the trypsin enzyme in spectral counting. We further demonstrated that the use of pepsin can effectively correct such bias. In addition, we discovered that pepsin digestion can better identify keratin-associated proteins than trypsin proteolysis, which offers another effective tool for studying the hair proteome.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it