PEP Search in MyCompoundID: Detection and Identification of Dipeptides and Tripeptides Using Dimethyl Labeling and Hydrophilic Interaction Liquid Chromatography Tandem Mass Spectrometry
Bibliographic record
Abstract
Small peptides, such as dipeptides and tripeptides, are naturally present in many biological samples (e.g., human biofluids and cell extracts). They have attracted great attention in many research fields because of their important biological functions as well as potential roles as disease biomarkers. Tandem mass spectrometry (MS/MS) can be used to profile these small peptides. However, the type and number of fragment ions generated in MS/MS are often limited for unambiguous identification. Herein we report a novel database-search strategy based on the use of MS/MS spectra of both unlabeled and dimethyl labeled peptides to identify and confirm amino acid sequences of di/tripeptides that are separated using hydrophilic interaction (HILIC) liquid chromatography (LC). To facilitate the di/tripeptide identification, a database consisting of all the predicted MS/MS spectra from 400 dipeptides and 8000 tripeptides was created, and a search tool, PEP Search, was developed and housed at the MyCompoundID website ( www.mycompoundid.org/PEP). To evaluate the identification specificity of this method, we used acid hydrolysis to degrade a standard protein, cytochrome c, to produce many di/tripeptides with known sequences for LC/MS/MS. The resultant MS/MS spectra were searched against the database to generate a list of matches which were compared to the known sequences. We correctly identified the di/tripeptides in the protein hydrolysate. We then applied this method to detect and identify di/tripeptides naturally present in human urine samples with high confidence. We envisage the use of this method as a complementary tool to various LC/MS techniques currently available for small molecule or metabolome profiling with an added benefit of covering all di/tripeptide chemical space.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".