Methionine In and Out of Proteins: Targets for Drug Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing need for new antibiotics to overcome rapidly developing resistance mechanisms observed in clinical isolates of Gram-positive and Gram-negative eubacteria has placed critical emphasis on the search for new antibacterial enzyme targets and the structural and mechanistic investigation of such targets. Among these potential targets, the enzymes responsible for integrating the amino acid methionine into proteins, along with its subsequent post-translational modification and repair, have emerged as promising candidates for the development of novel antibiotics. As well, there is increasing evidence for the importance of several of these enzymes in the development of anti-cancer, anti-parasitic, and anti-atherosclerotic drugs. Within the last three years, the crystal structures of all of these enzymes have been determined, which offers an unprecedented source of structural information for inhibitor design. The development of combinatorial chemistry and high throughput screening procedures has quickly provided several potent, specific inhibitors for a number of these enzymes, particularly the peptide deformylase, methionine aminopeptidase, and methionyl-tRNA synthetase enzymes. This review critically analyzes the future potential for inhibition of enzymes in this pathway, allowing for a pragmatic view of the success of inhibitor developments and highlighting areas in which further investigations are warranted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it