P105-T Peptide Sequence Validation using Retention Time Information
Bibliographic record
Abstract
The increasing number of MS/MS spectra, generated by new mass spectrometers with fast scanning (LTQ, ThermoElectron, etc.), requires better and more robust spectra filtering. Retention time is one of the important peptide characteristics, but it is not used very often with the aim of peptide sequence validation. Here, we present an approach to filter Mascot (Matrixscience) search results on the basis of comparison of experimental retention times with predicted ones. Proteins were digested in-gel or in-solution with trypsin according to conventional protocols. Resulting peptide mixtures were analyzed on line with an LC-MS system (HP 1100 Nanoflow HPLC [Agilent] and LTQ Mass Spectrometer [ThermoElectron]). Peptides were separated on custom-packed 75-μm i.d., 10-cm-long PicoTip columns (New Objective) packed with 3.5-μm C18 beads (Pursuit, Varian). The column effluent was sprayed directly into the transfer tube of the mass spectrometer. The Sequence Specific Retention Calculator (http://hs2.proteome.ca/SSRCalc/SSRCalc.html) was used for calculation of peptide relative hydrophobicities. This program uses a refined model for determination of hydrophobicity, presented recently by Oleg Krokhin (University of Manitoba). Two columns are automatically added to the standard Mascot peptide summary page: scan number (reflecting retention time) and relative hydrophobicity. The plot of scan number vs. hydrophobicity is also created. The linear regression could be calculated with a subset of “good peptides” (having high Mascot score, e.g., >50). Based on this regression, predicted scan numbers could be identified. The rest of the peptides (with score <50) are tested, and sequences with scan numbers too far from predicted are rejected as false-positive hits. The algorithm was trained on standard digests such as BSA, and is now applicable to any LC-MS Mascot search results. Rejected hits were confirmed by manual inspection of MS/MS files. It appeared that this script is especially useful for validation of singly charged peptides, which often give fragmentation spectra of poor quality.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".