Identifying Specific Small-Molecule Interactions Using Electrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
A simple method for establishing whether complexes composed of small molecules detected by electrospray ionization mass spectrometry (ES-MS) originate from specific interactions in solution or nonspecific binding during the ES process is described. The technique, referred to as the nonspecific probe method, exploits the tendency of small molecules to bind nonspecifically to macromolecules during the ES process to establish the presence of specific noncovalent interactions. To implement the method, a macromolecule probe (P(NS)), which does not bind specifically to any of the components present in solution, is added prior to ES-MS analysis. The existence of specific small-molecule complexes is determined from an analysis of the measured distributions of the small molecules bound nonspecifically to P(NS). The principal assumption on which this methodology is based is that nonspecific binding of small molecules and their complexes to P(NS) during ES is a statistical (random) process. A mathematical framework for establishing the presence of specific heterocomplexes is presented. The reliability of the method for distinguishing specific from nonspecific small-molecule interactions is illustrated for peptide-antibiotic and metal ion-ligand interactions in water.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".