Capture Compound Mass Spectrometry: A Technology for the Investigation of Small Molecule Protein Interactions
Bibliographic record
Abstract
One of the major hurdles in the post-genomic era is to understand the function of genes and the interplay of many different cellular proteins. This is especially important for drug development. Capture compound mass spectrometry (CCMS) addresses this challenge by selectively reducing the complexity of the proteome. Capture compounds are trifunctional molecules: a selectivity function reversibly interacts via affinity with proteins; a reactivity function irreversibly forms a covalent bond outside the affinity binding site; and a sorting/pullout function allows the captured protein(s) to be isolated from cellular lysate for mass spectrometric analysis and characterization by database queries. In the present study, we demonstrate the use of a CCMS capture compound with a sulfonamide drug analog as its selectivity function, isolating an expected target protein from cell lysates containing a large excess of other "non-target" proteins. A future application of CCMS is to define or confirm drug target proteins and their mechanisms of drug action, or to discover off-target proteins that cause side effects, enabling subsequent drug structure optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".