Tobacco budworm dihydrofolate reductase is a promising target for insecticide discovery
Bibliographic record
Abstract
Structural differences in dihydrofolate reductases from different species have been exploited to develop specific inhibitory molecules, such as chemotherapeutic agents, antibiotics or antihelminthics, that show species specificity or selectivity. As dihydrofolate reductase (DHFR) is a crucial enzyme for the synthesis of purines, pyrimidines and some amino acids, and also because developing insects show a remarkably rapid rate of cell division, DHFR is a potentially promising target for the discovery of novel insecticides. We have thus isolated and characterized the enzyme from a serious agricultural pest, Heliothis (Helicoverpa) virescens, the tobacco budworm. Sequencing tryptic peptides of the 35 000-fold purified DHFR allowed the subsequent isolation of a partial cDNA, with the full Dhfr gene sequence obtained from a genomic library. The H. virescens Dhfr spans 4 kb, with three introns, and encodes 185 amino acids. The enzyme shows an overall similarity of approximately 68% with DHFR from other metazoans, which has facilitated the molecular modeling of the protein. DHFRs from insects appear to have strikingly reduced sensitivity to inhibition by methotrexate, compared with the vertebrate enzymes, and this reduction was also reflected in the total binding energy seen after modeling experiments. Four residues that may be characteristic of insect DHFR, as well as a unique cysteine in the H. virescens DHFR active site, offer insight into the nature of inhibitor selectivity and provide suitable target sites for insecticide discovery.
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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 | 0.000 | 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 | 0.000 |
| 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".