Structural basis for the high <i>all-trans</i> -retinaldehyde reductase activity of the tumor marker AKR1B10
Bibliographic record
Abstract
AKR1B10 is a human aldo-keto reductase (AKR) found to be elevated in several cancer types and in precancerous lesions. In vitro, AKR1B10 exhibits a much higher retinaldehyde reductase activity than any other human AKR, including AKR1B1 (aldose reductase). We here demonstrate that AKR1B10 also acts as a retinaldehyde reductase in vivo. This activity may be relevant in controlling the first step of retinoic acid synthesis. Up-regulation of AKR1B10, resulting in retinoic acid depletion, may lead to cellular proliferation. Both in vitro and in vivo activities of AKR1B10 were inhibited by tolrestat, an AKR1B1 inhibitor developed for diabetes treatment. The crystal structure of the ternary complex AKR1B10-NADP(+)-tolrestat was determined at 1.25-A resolution. Molecular dynamics models of AKR1B10 and AKR1B1 with retinaldehyde isomers and site-directed mutagenesis show that subtle differences at the entrance of the retinoid-binding site, especially at position 125, are determinant for the all-trans-retinaldehyde specificity of AKR1B10. Substitutions in the retinaldehyde cyclohexene ring also influence the specificity. These structural features should facilitate the design of specific inhibitors, with potential use in cancer and diabetes treatments.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".