<i>myo</i>-Inositol phosphate isomers generated by the action of a phytate-degrading enzyme from <i>Klebsiella terrigena</i> on phytate
Bibliographic record
Abstract
For the first time a dual pathway for dephosphorylation of myo-inositol hexakisphosphate by a histidine acid phytase was established. The phytate-degrading enzyme of Klebsiella terrigena degrades myo-inositol hexakisphosphate by stepwise dephosphorylation, preferably via D-Ins(1,2,4,5,6)P5, D-Ins(1,2,5,6)P4, D-Ins(1,2,6)P3, D-Ins(1,2)P2 and alternatively via D-Ins(1,2,4,5,6)P5, Ins(2,4,5,6)P4, D-Ins(2,4,5)P3, D-Ins(2,4)P2 to finally Ins(2)P. It was estimated that more than 98% of phytate hydrolysis occurs via D-Ins(1,2,4,5,6)P5. Therefore, the phytate-degrading enzyme from K. terrigena has to be considered a 3-phytase (EC 3.1.3.8). A second dual pathway of minor importance could be proposed that is in accordance with the results obtained by analysis of the dephosphorylation products formed by the action of the phytate-degrading enzyme of K. terrigena on myo-inositol hexakisphosphate. It proceeds preferably via D-Ins(1,2,3,5,6)P5, D-Ins(1,2,3,6)P4, Ins(1,2,3)P3, D-Ins(2,3)P2 and alternatively via D-Ins(1,2,3,5,6)P5, D-Ins(2,3,5,6)P4, D-Ins(2,3,5)P3, D-Ins(2,3)P2 to finally Ins(2)P. D-Ins(2,3,5,6)P4, D-Ins(2,3,5)P3, and D-Ins(2,4)P2 were reported for the first time as intermediates of enzymatic phytate dephosphorylation. A role of the phytate-degrading enzyme from K. terrigena in phytate breakdown could not be ruled out. Because of its cytoplasmatic localization and the suggestions for substrate recognition, D-Ins(1,3,4,5,6)P5 might be the natural substrate of this enzyme and, therefore, may play a role in microbial pathogenesis or cellular myo-inositol phosphate metabolism.
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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) | 0.000 | 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".