Complete characterization of the mutation landscape reveals the effect on amylin stability and amyloidogenicity
Bibliographic record
Abstract
Type-II diabetes is believed to be partially aggravated by the emergence of toxic amylin protein deposits in the extracellular space of the pancreas β-cells. Amylin, the regulatory hormone that is co-secreted with insulin, has been observed to misfold into toxic structures. Pramlintide, an FDA approved injectable amylin analog mutated at positions 25, 28, and 29 was therefore developed to create a more stable, soluble, less-aggregating, and equipotent peptide that is used as an adjunctive therapy for diabetes. However, because Pramlintide is not ideal, researchers have been exploring other amylin analogs as therapeutic replacements. In this work, we assist the finding of optimal analogs by computationally revealing the mutational landscape of amylin. We computed the structure energies of all possible single-point mutations and studied the effect they have on amylin stability and amyloidogenicity. Each of the 37 amylin residues was mutated in silico into the 19 canonical amino acids and an energy function computing the Lennard-Jones, Coulomb and solvation energy was used to analyze changes in stability. The mutation landscape identified amylin's conserved stable regions, residues that can be tweaked to further stabilize structure, regions that are susceptible to mutations, and mutations that are amyloidogenic. We used the single-point mutational landscape data to generate estimations for higher-order multiple-point mutational landscapes and discovered millions of three-point mutations that are more stable and less amyloidogenic than Pramlintide. The landscapes provided an explanation for the effect of the S20G and Q10R mutations on the onset of diabetes of the Chinese and Maori populations, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".