Methionine In and Out of Proteins: Targets for Drug Design
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".