Inhibition of Protein N-Myristoylation: A Therapeutic Protocol in Developing Anticancer Agents
Bibliographic record
Abstract
N-myristoyltransferase (NMT) is an essential eukaryotic enzyme which catalyzes the transfer of the myristoyl group to the terminal glycine residue of a number of proteins including those involved in signal transduction and apoptotic pathways. Myristoylation is crucial for the cellular proliferation process and is required for the growth and development in a number of organisms including many human pathogens and viruses. Targeting the myristoylation process thus has emerged as a novel therapeutic strategy for anticancer drug design. The expression/activity of NMT is considerably elevated in a number of cancers originating in the colon, stomach, gallbladder, brain and breast and attenuation of NMT levels has been shown to induce apoptosis in cancerous cell lines and reduce tumor volume in murine xenograft models for cancer. A focus of current therapeutic interventions in novel cancer treatments is therefore directed at developing specific NMT inhibitors. The inhibition of the myristoyl lipidation process with respect to cancer drug development lies in the fact that many proteins involved in oncogenesis such as src and various kinases require myristoylation to perform their cellular functions. Inhibiting NMT functions to control malignancy is a novel approach in the area of anticancer drug design and there are rapidly expanding discoveries of synthetic NMT inhibitors as potential chemotherapeutic agents to be employed in the warfare against cancer. The current review focuses on developments of various chemical NMT inhibitors with potential roles as anticancer agents. Keywords: Cancer, chemotherapeutic agents, NMT inhibitors, N-myristoyltransferase, oncogenesis, Bischloroethylnitrosourea, Coenzyme A, Candida albicans N-myristoyltransferase, Diaminedichloroplatinum, Focal adhesion kinase, Glioblastoma multiforme, Histidine decarboxylase, Myristoylated protein, Oral squamous carcinoma, Pancreatic carcinoma
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".