In-Source Reduction of Disulfide-Bonded Peptides Monitored by Ion Mobility Mass Spectrometry
Bibliographic record
Abstract
Many peptides with antimicrobial activity and/or therapeutic potential contain disulfide bonds as a means to enhance stability, and their quantitation is often performed using electrospray ionization mass spectrometry (ESI-MS). Disulfides can be reduced during ESI under commonly used instrument conditions, which has the potential to hinder accurate peptide quantitation. We demonstrate that this in-source reduction (ISR) is predominantly observed for peptides infused from acidic solutions and subjected to elevated ESI voltages (3-4 kV). ISR is readily apparent in the mass spectrum of oxytocin-a small, single disulfide-containing peptide. However, subtle m/z shifts due to partial ISR of highly charged (z ≥ 3) peptides with multiple disulfide linkages may proceed unnoticed. Ion mobility (IM)-MS separates ions on the basis of charge and shape in the gas phase, and using insulin as a model system, we show that IM-MS arrival time distributions (ATDs) are particularly sensitive to partial ISR of large peptides. Isotope modeling allows for the relative quantitation of disulfide-intact and partially reduced states of the mobility-separated peptide conformers. Interestingly, hepcidin peptides ionized from acidic solutions at elevated ESI voltages undergo gas-phase compaction, ostensibly due to partial disulfide ISR. Our IM-MS results lead us to propose that residual acid is the likely cause of disparate ATDs recently measured for hepcidin from different suppliers [Anal. Bioanal. Chem. 409, 2559-2567 (2017)]. Overall, our results demonstrate the utility of IM-MS to detect partial ISR of disulfide-bonded peptides and reinforce the notion that peptide/protein measurements should be carried out using minimally activating instrument conditions. Graphical Abstract ᅟ.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".