Top‐down characterization of proteins and drug‐protein complexes using nanoelectrospray tandem mass spectrometry
Bibliographic record
Abstract
We report a 'top-down' approach for characterization of proteins, and identification of binding sites in protein-drug complexes using nanoelectrospray ionization hybrid quadrupole time-of-flight tandem mass spectrometry (nanoESI-MS/MS). The efficiency of direct fragmentation of intact protein ions and the feasibility of this method were initially demonstrated using several well-characterized proteins with different molecular weights including metallothionein (6126 Da), cytochrome c (horse, 12360 Da), myoglobin (horse, 16592 Da), and hemoglobin (human, 64453 Da). Simply varying collision energy without enzyme digestion and gel or LC separation generated a range of peptide fragments of these proteins. Over 80% of these peptide ions matched those in the SWISS-PROT database with mass accuracy of 8 to 32 ppm with external calibration. This technique was further applied to fragment a cisplatin-metallothionein complex to identify the binding sites, demonstrating a potential application in the study of drug-protein binding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".