Mass spectrometric study of the effects of hydrophobic surface chemistry and morphology on the digestion of surface-bound proteins
Bibliographic record
Abstract
Our previous work has demonstrated that reversed-phase chromatographic micro-beads can be used to capture proteins from complex biological matrices and the surface-bound proteins can be enzymatically digested for protein identification by mass spectrometry (MS). Here we examine the peptides generated from digestion of proteins bound to various types of micro-bead surfaces in order to determine the effects of surface chemistry and surface morphology on the digestion process. Detailed examinations of site cleavages and sequence coverage are carried out for a tryptic digestion of cytochrome c adsorbed on reversed-phase polystyrene divinylbenzene (Poros R2 beads) versus C(18) bonded-phase silica beads. It is shown that although the surface does not completely hinder the digestion of cleavage sites of the protein, the digestion products are clearly different than those obtained from a solution digest. Specifically, a partial digestion results from surface digestion, resulting in a greater number of missed cleavages than a comparable solution digest. Subsequent comparisons of peptide mass maps generated from the digestion of various proteins on surfaces with altering chemistry (C(4), C(8), C(18), and R2 beads), or with different surface morphology, were performed. The results reveal that surface chemistry plays only a minor role in affecting the peptide mass maps, and surface morphology had no noticeable effects on the resulting peptide mass maps. It is also shown that the mass spectrometric detection method used to analyze the digested peptides can significantly influence the information content on cleavage sites and the extent of sequence coverage. The use of a combination of MALDI, LC/off-line MALDI, and LC/ESI MS is demonstrated to be crucial in revealing subtle changes in the peptide mass maps.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".