An engineered tryptophan zipper‐type peptide as a molecular recognition scaffold
Bibliographic record
Abstract
In an effort to develop a structured peptide scaffold that lacks a disulfide bond and is thus suitable for molecular recognition applications in the reducing environment of the cytosol, we investigated engineered versions of the trpzip class of beta-hairpin peptides. We have previously shown that even most highly folded members of the trpzip class (i.e. the 16mer peptide HP5W4) are substantially destabilized by the introduction of mutations in the turn region and therefore not an ideal peptide scaffold. To address this issue, we used a FRET-based live cell screening system to identify extended trpzip-type peptides with additional stabilizing interactions. One of the most promising of these extended trpzip-type variants is the 24mer xxtz1-peptide with the sequence KAWTHDWTWNPATGKWTWLWRKNK. A phage display library of this peptide with randomization of six residues with side chains directed towards one face of the hairpin was constructed and panned against immobilized streptavidin. We have also explored the use of xxtz1-peptide for the presentation of an unstructured peptide 'loop' inserted into the turn region. Although NMR analysis provided no direct evidence for structure in the xxtz1-peptide with the loop insertion, we did attempt to use this construct as a scaffold for phage display of randomized peptide libraries. Panning of the resulting libraries against streptavidin resulted in the identification of peptide sequences with submicromolar affinities. Interestingly, substitution of key residues in the hairpin-derived portion of the peptide resulted in a 400-fold decrease in K(d), suggesting that the hairpin-derived portion plays an important role in preorganization of the loop region for molecular recognition.
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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) | 0.001 | 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".