A novel mass spectrometry-based assay for GSK-3β activity
Bibliographic record
Abstract
BACKGROUND: As a component of the progression from genomic to proteomic analysis, there is a need for accurate assessment of protein post-translational modifications such as phosphorylation. Traditional kinase assays rely heavily on the incorporation of gamma-P32 radiolabeled isotopes, monoclonal anti-phospho-protein antibodies, or gel shift analysis of substrate proteins. In addition to the expensive and time consuming nature of these methods, the use of radio-ligands imposes restrictions based on the half-life of the radionucleotides and pose potential health risks to researchers. With the shortcomings of traditional assays in mind, the aim of this study was to develop a high throughput, non-radioactive kinase assay for screening Glycogen Synthase Kinase-3beta (GSK-3beta) activity. RESULTS: Synthetic peptide substrates designed with a GSK-3beta phosphorylation site were assayed with both recombinant enzyme and GSK-3beta immunoprecipitated from NIH 3T3 fibroblasts. A molecular weight shift equal to that of a single phosphate group (80 Da.) was detected by surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) in a GSK-3beta target peptide (2B-Sp). Not only was there a dose-dependent response in molecular weight shift to the amount of recombinant GSK-3beta used in this assay, this shift was also inhibited by lithium chloride (LiCl), in a dose-dependent manner. CONCLUSION: We present here a novel method to sensitively measure peptide phosphorylation by GSK-3beta that, due to the incorporation of substrate controls, is applicable to either purified enzyme or cell extracts. Future studies using this method have the potential to elucidate the activity of GSK-3beta in vivo, and to screen enzyme activity in relation to a variety of GSK-3beta related disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".