Materials Screening for Sol–Gel-Derived High-Density Multi-Kinase Microarrays
Bibliographic record
Abstract
Protein microarrays based on pin-printing of sol–gel-entrapped biomolecules have emerged as a potential tool to accelerate drug screening and discovery. However, while materials have recently been identified that are suitable for printing of high-density sol–gel-based microarrays, the ability to print arrays of delicate proteins such as kinases, and to assay their activity and inhibition on-array, has yet to be demonstrated. In this study, we have performed a criteria-based directed screen of sol–gel-based materials to identify compositions that are suitable for the fabrication of high-density, multikinase microarrays. Printable formulations were assessed for their compatibility with a fluorescent, phosphospecific dye used as an end-point indicator for on-array kinase assays, including an assessment of the effects of spot size (100 μm vs 400 μm) and slide surface chemistry on signal reproducibility. The combinations of materials, surfaces, and spot sizes that were found to be compatible with reproducible signal generation were evaluated for their ability to retain the activity of a range of kinases, which were co-entrapped with their respective substrates into the optimal sol–gel materials to produce microarrays. Ultimately, two material/surface combinations, from potentially thousands, were identified, one of which was used to produce a robust, highly active kinase microarray that could be used for qualitative screening as well as quantitative inhibition assays.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".