Preparation and Assay of Myristoyl-CoA:Protein N-Myristoyltransferase
Bibliographic record
Abstract
Myristoyl-CoA:protein N -myristoyltransferase (NMT) catalyzes the cotranslational transfer of myristate from myristoyl-CoA to the amino-terminal glycine residue of a number of cellular, viral, fungal, and oncoproteins ( 1 – 5 ). These proteins include the catalytic subunit of cAMP-dependent protein kinase, various tyrosine kinases (including pp60 src , pp60 yes , pp56 lck , pp59 fyn/syn , and cAbl), the β-subunit of calmodulin-dependent protein phosphatase (calcineurin), the myristoylated alanine rich C kinase substrate, the α-subunit of several G-proteins, and several ARF proteins involved in ADP ribosylation ( 1 – 5 ). A significant problem with the purification and characterization of NMT concerns the available assay procedures. The wide availability and use of peptides allow for convenient substrates. However, the existing procedures to isolate and analyze the reaction products of myristoylation, such as reverse-phase HPLC, are expensive and time-consuming ( 6 , 7 ). Ion-exchange column chromatography, differential solubilization, adsorption of myristoyl-CoA to acidic alumina, and ion-exchange exclusion of [ 3 H]myristoyl peptides have been utilized as assay methods are faster and more convenient than reverse-phase HPLC ( 8 – 10 ). However, these procedures are still limited with respect to the number of assays that can conveniently be performed. Here we described a new rapid, reliable, and inexpensive NMT assay based on the binding of [ 3 H]myristoyl-peptide to a P81 phosphocellulose paper matrix ( 11 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".