The Interplay of Disulfide Bonds, α-Helicity, and Hydrophobic Interactions Leads to Ultrahigh Proteolytic Stability of Peptides
Bibliographic record
Abstract
The contribution of noncovalent interactions to the stability of naturally occurring peptides and proteins has been generally acknowledged, though how these can be rationally manipulated to improve the proteolytic stability of synthetic peptides remains to be explored. In this study, a platform to enhance the proteolytic stability of peptides was developed by controllably dimerizing them into α-helical dimers, connected by two disulfide bonds. This platform not only directs peptides toward an α-helical conformation but permits control of the interfacial hydrophobic interactions between the peptides of the dimer. Using two model dimeric systems constructed from the N-terminal α-helix of RNase A and known inhibitors for the E3 ubiquitin ligase MDM2 (and its homologue MDMX), a deeper understanding into the interplay of disulfide bonds, α-helicity, and hydrophobic interactions on enhanced proteolytic stability was sought out. Results reveal that all three parameters play an important role on attaining ultrahigh proteolytic resistance, a concept that can be exploited for the development of future peptide therapeutics. The understanding gained through this study will enable this strategy to be tailored to new peptides because the proposed strategy displays substantial tolerance to sequence permutation. It thus appears promising for conveniently creating prodrugs composed entirely of the therapeutic peptide itself (i.e., in the form of a dimer).
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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 | 0.000 | 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".