Bio-Solid-Phase Extraction/Tandem Mass Spectrometry for Identification of Bioactive Compounds in Mixtures
Bibliographic record
Abstract
We describe a two-step column-based bioassay method with tandem mass spectrometric detection for rapid identification of bioactive species in mixtures. The first step uses an immobilized enzyme reactor (IMER) column interfaced to an electrospray ionization mass spectrometer (ESI-MS) to identify mixtures containing bioactive compounds (i.e., enzyme inhibitors), while the second step uses bioselective solid-phase extraction (bioSPE) columns to isolate compounds from "hit" mixtures, which are then identified online by data-dependent ESI-MS. IMER columns were prepared by entrapment of adenosine deaminase (ADA) into sol-gel derived monolithic silica columns, and used to perform a primary IMER screen of mixtures prepared from a bioactive library, which resulted in four apparent hit compounds. Such columns did not provide sufficient binding site density to allow bioSPE, and thus a new column format was developed using ADA that was covalently immobilized to monolithic silica capillary columns, providing ∼500-fold more protein binding sites than were present in columns containing entrapped proteins. Using the covalently linked ADA columns, bioactive mixtures identified by IMER were infused until a maximum total ion current was achieved, followed by washing with a buffer to remove unbound compounds. A harsh wash with 3% acetic acid eluted the strongly bound ligands and the resulting peak triggered data dependent MS/MS to identify the ligand, showing that two of the apparent hits were true ADA inhibitors and demonstrating the ability of this method to rapidly identify bioactive compounds in mixtures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".