Analysis of protein mixtures by electrospray mass spectrometry: Effects of conformation and desolvation behavior on the signal intensities of hemoglobin subunits
Bibliographic record
Abstract
The determination of solution-phase protein concentration ratios based on ESI-MS intensity ratios is not always straightforward. For example, equimolar mixtures of hemoglobin alpha- and beta-subunits consistently result in much higher peak intensities for the alpha-chain. The current work explores the origin of this effect. Under mildly acidic conditions (pH 3.4) alpha-globin is extensively unfolded, whereas beta-globin retains residual structure. Because of its greater nonpolar character, the more unfolded alpha-subunit can more effectively compete for charge. This leads to suppression of beta-globin signals under conditions where the protein ion yield is limited by the charge concentration on the initially formed ESI droplets. More balanced intensities are observed when operating under charge excess conditions and/or in a solvent environment where both proteins are unfolded to a similar degree (pH 2.2). However, even in these cases the overall alpha-globin peak intensity is still twice as high as that of the beta-subunit. The persistent imbalance under these conditions originates from the different declustering behaviors of the two proteins. A considerable fraction of beta-globin undergoes incomplete desolvation during ESI, thereby reducing the intensity of bare [beta + zH](z+) ions. When including the contributions of incompletely desolvated species, the overall alpha:beta ion intensity ratio is close to unity. The alpha:beta intensity imbalance can also be eliminated by a strongly elevated declustering potential in the ion sampling interface. In conclusion, important factors that have to be considered for the ESI-MS analysis of protein mixtures are (1) conformational effects, resulting in differential surface activities, and (2) dissimilarities in the protein desolvation behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".