Effect of solvent and electrospray mass spectrometer parameters on the charge state distribution of peptides – a case study using liquid chromatography/mass spectrometry method development for beta‐endorphin assay
Bibliographic record
Abstract
Beta-endorphin was used as a model peptide to study the effect of solvent and electrospray mass spectrometer parameters in the optimisation of an assay method for multiply charged compounds using liquid chromatography/mass spectrometry (LC/MS). Unlike with singly charged compounds, the charge state distribution has a significant impact in the method development of multiply charged compounds such as peptides. Using a 50% acetonitrile/water solvent mixture, we found that the ion spray voltage had no influence on the charge state distribution. However, increasing declustering potential led to deprotonation of the higher charge states of the peptide thus causing a shift to lower charge states. The mechanism leading to the deprotonation was examined. It was concluded that the deprotonation is due to endoergic proton transfer from the peptide to solvent molecules clustered to the peptide that occurs in the declustering region. The extent of deprotonation increases with increasing proton affinity of the molecules of the non-aqueous solvent component used. Thus, if desired, deprotonation can be avoided by selecting a low proton affinity solvent such as methanol. The focusing potential was also found to have a great influence on the charge state distribution observed. The results of this study enabled us to select the optimum ion to be used in single ion/reaction monitoring mode. They also provided the most favourable parameter values to be used in the method to obtain the best sensitivity for the ion of choice. The results demonstrate the importance of considering the charge state distribution in the optimisation of electrospray LC/MS methods for multiply charged compounds.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".