Design Novel Selective Inhibitors of Class II Fructose‐1,6‐bisphosphate Aldolase
Bibliographic record
Abstract
Fructose‐1,6‐bisphosphate (FBP) aldolase (E.C. ) catalyzes the reversible aldol condensation of dihydroxyacetonephosphate (DHAP) and glyceraldehyde‐3‐phosphate in glycolysis, gluconeogenesis and Calvin cycle. FBP aldolases are categorized into two groups based on different catalytic mechanisms: Class I aldolases form a Schiff‐base intermediate with the substrate through an active site lysine residue, whereas Class II aldolases contain a divalent metal which coordinates and stabilizes the carbanion intermediate. Noticeably, Class II aldolase does not exist in animals or plants, making this enzyme a potential drug target for pathogenic microbial organisms such as Mycobacterium tuberculosis , Magnaporthe grisea , Pseudomonas aeruginosa and Bacillus anthracis . Our research group has found that a commercially available antidote for heavy metal poisoning is a good competitive inhibitor of Class II FBP aldolase. In order to improve the binding affinity of this inhibitor for the enzyme activate site, new molecules based upon this compound were designed, synthesized and tested against recombinant Class II FBP aldolases from aforementioned microorganisms using kinetic assays developed by our group. This research is supported by NSERC (J.G.G. and G.I.D.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".