Prediction of the Charge States of Folded Proteins in Electrospray Ionization
Bibliographic record
Abstract
Earlier work from this laboratory dealt with the observation that the charge states of non-denatured proteins can be decreased by use of buffer salts in which the gas-phase basicity of conjugate base B, GB(B), of the buffer cations is high. A theoretical model was developed and applied to several small proteins. The predictions of the charge states were found to be in good agreement with those observed experimentally. Because the computational model is based on the charge residue model (CRM), the observed agreement lends support for the CRM. In the present work, the same model is applied to recent data by Catalina et al. who showed that very large charge reductions are achieved with very high GB(B) proton sponges. Their data included lysozyme but also the very much larger proteins, p-hydroxybenzoate hydroxylase (PHBH), 90 kDa and glutamate synthase (GLTS), 166 kDA. The present work examines the performance of the model for the much stronger bases and the very much larger proteins. It is found that the predictions of the charge states agree well for the small protein lysozyme but somewhat less well with the experimental results for PHBH and GLTS. The causes for the lack of good agreement with the large proteins are examined.
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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) | 0.000 | 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 | 0.001 | 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 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".