Assessing Gαq/15-signaling with IP-One: Single Plate Transfection and Assay Protocol for Cell-Based High-Throughput Assay
Bibliographic record
Abstract
Cell-based functional assays are an important part of compound screening and drug lead optimization, and they can also play a crucial role in the determination of the residues involved in ligand binding and signaling for a particular G-protein-coupled receptor.Conventional methods used for Gαq/15coupled receptors rely on the use of fluorescent probes for Ca ++ sensing (such as Fura-2 and Fluo-4) or on the incorporation of [ 3 H]-inositol into inositol 1,4,5-triphosphate (IP3).However, these methods are not suitable for screening large libraries of compounds or for screening several mutants of the same receptor.In contrast, the IP-One assay by Cisbio is a TR-FRET assay suitable for large compound library screening when using stable cell lines that express a specific 7TMR.However, when using transiently transfected mutants of a 7TMR, this assay is not ideal, as it requires a two-step protocol of cell culture.Therefore, we have optimized the IP-One assay protocol using the reverse transfection method in 384well plates.This offers a time-and resource-efficient alternative to the two-step protocol previously used for the screening of several mutants of Gαq/15-coupled 7TMRs. Keywords: G-protein-
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.033 |
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".