Comparison of the Human Plasma Peptides from the Fit of Fragmentation Spectra versus Accurate Monoisotopic Precursor Mass
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In nature, ionized peptides with heavy isotopes and hydrogen rearrangements show a broad mass distribution with signals at discrete delta mass values from −3 to +5 Da by mass spectrometry (MS). For many peptides, the intensity of the +1 or +2 Da isotope exceeds the signal from the monoisotopic mass. Therefore, there is a need for a method that improves peptide identification from heavy isotopes or hydrogen rearrangements based on the fit of tandem mass spectra. Peptides may be identified using an accurate monoisotopic precursor mass with ≤0.1 Da. However, many peptides with heavy isotopes and H-loss can be identified and enumerated based on the fit of their MS/MS spectra alone in the absence of an accurate precursor monoisotopic mass (i.e., ± 3 Da) using the X!TANDEM MS/MS fitting algorithm. In this study, human plasma samples were analyzed with a highly resolving axially harmonic orbital ion trap (OIT) and a sensitive linear quadrupole ion trap (LIT). The MS/MS fragmentation spectra from the OIT can be fit to peptides from the monoisotopic (±0.1 Da) as well as all other precursor masses with a wide mass tolerance (±3 Da). The resulting delta mass distribution can then be plotted and compared to the predicted distribution of heavy isotopes and hydrogen rearrangements to provide a direct biophysical prediction and test the validity of the fit determined by accepting the best-fit MS/MS spectra. The OIT instrument, which has greater resolution, was sampled at 30 nL per minute, while the more sensitive LIT was sampled at 200 nL per minute. The MS/MS spectra generated by each instrument were fit to peptides within a wide window (±3 Da) using the rigorous X!TANDEM algorithm. The OIT and LIT results were compared in an SQL Server database and corrected against analytical and statistical controls. The delta mass distribution of the peptides with hydrogen rearrangements and heavy isotopes was determined from the fit MS/MS spectra using the R statistical program. The OIT sampled MS and MS/MS spectra from the high-intensity precursor ions by focusing on E7 to E9 detector counts. In contrast, the LIT sampled a range of precursor ion intensities focused from E4 to E7 and thus reached lower ion intensity values. As expected, the precursor mass [M + H] + obtained by the OIT exhibited sharp delta mass peaks at −3, −2, −1, 0, +1, +2, +3, +4, and +5 Da due to naturally occurring heavy isotopes and hydrogen rearrangements. The collection of peptides and proteins identified by OIT and LIT was in qualitative and quantitative agreement with one another, with 99.9% overlap on 2726 protein gene symbols from human plasma and a highly significant relationship by regression analysis. The protein p -values, false discovery rate q -values, and comparisons to the noise MS/MS analytical control and random MS/MS statistical control confirmed the high-confidence MS/MS identifications from both instruments. MS/MS fragmentation spectra from the OIT were fit to peptides. The resulting precursor ion delta mass distribution showed a precise match to the predicted isotope distributions and hydrogen rearrangements of natural peptides. Thus, analysis of delta mass plots provided powerful biophysical evidence for the accuracy of plasma peptide identification from the fit of the MS/MS spectra alone. The high level of agreement on proteins and peptides and the proportional enumeration between proteins identified by the OIT and those identified independently using a LIT confirmed that plasma peptides and proteins may be identified and quantified from MS/MS spectra alone without the need for an accurate measure of the precursor mass. The greater sensitivity and low cost of searching MS/MS spectra in the absence of an accurate mass mean that it is possible to identify and quantify more proteins for the discovery of proteins in clinical populations.
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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 | 0.000 |
| 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".