Protein-Doped Monolithic Silica Columns for Capillary Liquid Chromatography Prepared by the Sol−Gel Method: Applications to Frontal Affinity Chromatography
Bibliographic record
Abstract
The development of bioaffinity chromatography columns that are based on the entrapment of biomolecules within the pores of sol-gel-derived monolithic silica is reported. Monolithic nanoflow columns are formed by mixing the protein-compatible silica precursor diglycerylsilane with a buffered aqueous solution containing poly(ethylene oxide) (PEO, MW 10,000) and the protein of interest and then loading this mixture into a fused-silica capillary (150-250-microm i.d.). Spinodal decomposition of the PEO-doped sol into two distinct phases prior to the gelation of the silica results in a bimodal pore distribution that produces large macropores (>0.1 microm), to allow good flow of eluent with minimal back pressure, and mesopores (approximately 3-5-nm diameter) that retain a significant fraction of the entrapped protein. Addition of low levels of (3-aminopropyl)triethoxysilane is shown to minimize nonselective interactions of analytes with the column material, resulting in a column that is able to retain small molecules by virtue of their interaction with the entrapped biomolecules. Such columns are shown to be suitable for pressure-driven liquid chromatography and can be operated at relatively high flow rates (up to 500 microL x min(-1)) or with low back pressures (<100 psi) when used at flow rates of 5-10 microL x min(-1). The clinically relevant enzyme dihydrofolate reductase was entrapped within the bioaffinity columns and was used to screen mixtures of small molecules using frontal affinity chromatography with mass spectrometric detection. Inhibitors present in compound mixtures were retained via bioaffinity interactions, with the retention time being dependent on both the ligand concentration and the affinity of the ligand for the protein. The results suggest that such columns may find use in high-throughput screening of compound mixtures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".