Nonspecific Protein−Carbohydrate Complexes Produced by Nanoelectrospray Ionization. Factors Influencing Their Formation and Stability
Bibliographic record
Abstract
Factors influencing the formation of nonspecific protein-carbohydrate complexes during the nanoelectrospray (nanoES) process have been investigated. Protonated and deprotonated nonspecific complexes of ubiquitin (Ubq) and protonated complexes of carbonic anhydrase (CA) with carbohydrates, ranging in size from mono- to tetrasaccharide, were produced by nanoES and detected with a Fourier transform ion cyclotron resonance mass spectrometer. Both the fraction of protein engaged in nonspecific binding with the carbohydrates and the number of carbohydrates bound to the protein increase with increasing carbohydrate concentration. At a given concentration of protein and carbohydrate, nonspecific binding is favored for small (mono- and disaccharide) or hydrophilic carbohydrates over larger or more hydrophobic molecules, which tend to form gaseous monomer or cluster ions by nanoES. However, the extent of nonspecific binding is insensitive to the structure of the protein, with similar distributions of nonspecific complexes observed for both CA and Ubq. Nonspecific association is also insensitive to the charge state of the complex. A comparable degree of binding is observed for complexes in their protonated and deprotonated forms. Furthermore, the number of bound ligands can exceed significantly the charge state of the complex. Thermal dissociation experiments performed on the gaseous nonspecific complexes reveal that their kinetic stability is sensitive to both the structure of the carbohydrate (i.e., mono- < di- < tri- < tetrasaccharide) and the protein (Ubq < CA) and to the charge state, although no simple relationship between stability and charge state was identified. Taken together, the results of this study suggest that neutral protein-carbohydrate interactions (e.g., hydrogen bonds) contribute significantly and, perhaps, predominantly to the formation and stabilization of the nonspecific complexes. A strategy to minimize the formation of the nonspecific complexes, which is based on the enhancement of gaseous carbohydrate ion formation through the addition of metal salts (e.g., CaCl2) to the nanoES solution, is demonstrated.
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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) | 0.002 | 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".