Mass spectrometry for structural analysis and quantification of the Major Urinary Proteins of the house mouse
Bibliographic record
Abstract
The Major Urinary Proteins (MUPs) of the house mouse, Mus musculus domesticus, are 18–19 kDa beta-barrel lipocalins that are involved in chemical communication between individuals. Many of them are excreted in urine where they play multiple roles, including coding of owner identity and transport, and slow release of bound volatile pheromones. One of them, darcin, is a pheromone in its own right and induces long-term memory for the identity and location of the scent mark owner. We have shown that mass spectrometric analysis of intact proteins, and their ion mobility behaviour, is capable of dissecting subtle structural differences between the members of this class of proteins. Moreover, mass spectrometric analysis of the intact proteins can contribute towards molecular phenotyping of MUPs. However, whilst allowing relative quantification, the ionisation propensity or gas phase properties of the individual MUPs may compromise absolute quantification. To solve the challenge of absolute quantification of MUP expression, we have designed and constructed a QconCAT built from endopeptidase LysC peptides that is capable of quantifying MUPs found in laboratory animal strains and some MUPs from wild caught individuals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".