Structural and enzymatic comparison of <i>Faecalibacterium prausnitzii</i> GH31 α-glycosidases
Bibliographic record
Abstract
The gut microbiome is home to thousands of species of bacteria, that are essential for human digestion, immunity, and physiology. Faecalibacterium prausnitzii makes up about 5% of a healthy human gut microbiome and a lower abundance of this bacterium has been found in patients with IBD and Crohn's disease. Among an extensive repertoire of carbohydrate active enzymes, F. prausnitzii has 2 GH31 -glycosidases, which are from the same family as Sucrase-Isomaltase and Maltase-Glucoamylase, human digestive enzymes with overlapping and distinguishing substrate specificities. This project aims to characterize the substrate specificity and preference of F. prausnitzii GH31 -glycosidases to better understand the structural features of GH31 enzymes and the biological capabilities of these bacteria. AlphaFoldV2.1.0 was used to create computational models of F. prausnitzii glycosidases, and the substrate specificity and kinetics parameters are reported. Structurally, these -glycosidases have the same identified conserved N-terminal and (/)8 barrel domains, but FpAG1 has an additional conserved domain of unknown function at the C-terminus which is not found in the FpAG2 structure. Both FpAG1 and FpAG2 have -glucosidase and oligo-1,6-glucosidase activity. The comparative kinetic studies show that FpAG1 has a greater preference for -1,6 glycosidic linkages, and FpAG2 has a greater preference for -1,4 glycosidic linkages. Gaining insight on the GH31 -glycosidases as a component of F. prausnitzii metabolism can further our understanding of this community in the human gut microbiome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".