An In Silico Approach to Enzymatic Synthesis of Fucooligosaccharides Using α-l-Fucosidase from Thermotoga maritima
Bibliographic record
Abstract
Fucooligosaccharides comprise the primary group of human milk oligosaccharides. Due to their beneficial properties, a series of synthetic methods have been proposed to obtain them. Enzymatic methods show great promise, and α-l-fucosidase from Thermotoga maritima has emerged as a powerful catalyst for their production. Nonetheless, the enzyme’s limited substrate scope has delayed its wider application. The present work aims to compare the relative reactivity of fucose, pNP-fucose, and ethyl-fucose, while also exploring the molecular interactions of these fucosyl-donors with the enzyme through a combination DFT and docking analysis. The HOMO-LUMO band gaps range from −7.14571 to −4.24429 eV, with α/β-pNP-fucose and α-fucose being the three most reactive compounds. Moderate association energies between −6.4 to −5.5 kcal·mol−1 were found in the docking analysis, with α-pNP-fucose and both anomers of ethyl-fucose demonstrating the poorest affinity. In the case of α/β-lactose affinity to the β-fucose/enzyme complex, no significant differences were shown. We conclude that the best fucosyl-donors for transfucosylation are those that maintain an enzyme affinity and reactivity similar to pNP-fucose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".