Exploring novel human pancreatic alpha-amylase inhibitors : a departure from carbohydrate-based therapeutics
Bibliographic record
Abstract
Human pancreatic α-amylase (HPA) catalyzes a key step in the degradation of ingested starch. Accordingly HPA activity has been positively correlated to post-prandial blood glucose levels and has been identified as a viable target for inhibition and the development of therapeutics towards the treatment of diabetes and obesity. This work directs the hunt away from traditional saccharide-based inhibitors, which represent all carbohydrate metabolism therapeutics currently in use, to novel inhibitors with improved selectivity and potency. In Chapter 2, the synthesis of flavonol-based HPA inhibitors based upon the structure of Montbretin A, a complex flavonol glycoside, is explored. Through the synthesis of a library of Montbretin A analogues we were able to identify an inhibitor of HPA with a KI of 44 nM that formed new interactions within the amylase active site. Chapter 3 details work on the peptide-based HPA inhibitor helianthamide previously isolated from the Caribbean Sea anemone Stichodactyla helianthus. Recombinant expression of helianthamide as a fusion peptide was achieved in Escherichia coli and Pichia pastoris. Kinetic analysis indicated that recombinant helianthamide is one of the most potent HPA inhibitors known to date, with a KI of 0.01 nM. Structural analysis of the recombinant material indicated that it contained three disulfide bonds in a 1-5, 2-4, 3-6 pattern. Site-directed mutagenesis of helianthamide indicated that disruption of disulfide bonds led to a large decrease in potency, while alanine variants of residues forming polar contacts with HPA’s active site residues led to smaller decreases in potency, indicating that the intact tertiary structure of helianthamide is necessary for blockage of the amylase active site. Small peptides were synthesized based on the sequence of helianthamide, but most showed modest or no inhibition of HPA.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".