Comparative Study of Three Diverse Glycogen Branching Enzymes for Efficient Generation of Highly Surface Branched Starch Granules with Enhanced Digestive Resistance
Bibliographic record
Abstract
Glycogen branching enzymes (GBEs) are widely applied to functionalize starch. However, modification of granular starch is challenging, and among GBEs, so far, only GBEs from Geobacillus thermoglucosidans ( Gt GBE) and Rhodothermus obamensis ( Ro GBE) have been used. To further develop their modification, starch granules of waxy, normal, and three types of high-amylose maize starches were treated with GBEs from Petrotoga mobilis ( Pm GBE), Rhodothermus marinus ( Rm GBE), and Ro GBE as a benchmark. Pm BE most effectively and rapidly added short branches, causing a reduced crystallinity and surface order of the starch granules. Furthermore, digestibility analysis indicated that Pm GBE boosted the content of resistant starch. Along with its high activity, Pm GBE showed a superior binding capacity to starch granules. Based on structural comparison, surface binding sites and the N-terminal domain of unknown function in Pm GBE are proposed to influence activity and substrate specificity. Thus, Pm GBE showed potential as an effective tool for the future modification of starch granules.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".