High-Throughput Screening for Enzyme Inhibitors Using Frontal Affinity Chromatography with Liquid Chromatography and Mass Spectrometry
Bibliographic record
Abstract
This work presents new frontal affinity chromatography (FAC) methodologies for high-throughput screening of compound libraries, designed to increase screening rates and improve sensitivity and ruggedness in performance. A FAC column constructed around the enzyme N-acetylglucosaminyltransferase V (GnT-V) was implemented in the identification of potential enzyme inhibitors from two libraries of trisaccharides. Effluent from the FAC column was fractionated, sequentially processed via LC/MS, and referenced to a similar analysis through a control FAC column lacking the enzyme. The resulting multidimensional data sets were compared across corresponding sample and control fractions to identify binders, in a semiautomated approach. A strong binder in the protonated form at m/z 795 was identified from the first library of 81 compounds, exhibiting an estimated Kd value of 0.3 microM. Other binders yielded Kd values ranging from 0.35 to 3.35 microM. To demonstrate the improvement in performance of this FAC-LC/MS approach over the conventional online FAC/MS approach, 15 compounds from this library were blended with a second library of 1000 synthetic trisaccharides and screened against GnT-V. All ligands in the 15-compound set were identified in this larger screen, and no ligands of greater affinity than compound 1 were found. Our results show that FAC-LC/MS is a reliable method for screening large compound libraries directly and useful for large-scale ligand discovery initiatives.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".