Fluorescence-based characterization of genetically encoded peptides that fold in live cells: progress toward a generic hairpin scaffold
Bibliographic record
Abstract
Binding proteins suitable for expression and high affinity molecular recognition in the cytoplasm or nucleus of live cells have numerous applications in the biological sciences. In an effort to add a new minimal motif to the growing repertoire of validated non-immunoglobulin binding proteins, we have undertaken the development of a generic protein scaffold based on a single &bgr;-hairpin that can fold efficiently in the cytoplasm. We have developed a method, based on the measurement of fluorescence resonance energy transfer (FRET) between a genetically fused cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP), that allows the structural stability of recombinant &bgr;-hairpin peptides to be rapidly assessed both in vitro and in vivo. We have previously reported the validation of this method when applied to a 16mer tryptophan zipper &bgr;-hairpin. We now describe the use of this method to evaluate the potential of a designed 20mer &bgr;-hairpin peptide with a 3rd Trp/Trp cross-strand pair to function as a generic protein scaffold. Quantitative analysis of the FRET efficiency, resistance to proteolysis (assayed by loss of FRET), and circular dichroism spectra revealed that the 20mer peptide is significantly more tolerant of destabilizing mutations than the 16mer peptide. Furthermore, we experimentally demonstrate that the in vitro determined &bgr;-hairpin stabilities are well correlated with in vivo &bgr;-hairpin stabilities as determined by FRET measurements of colonies of live bacteria expressing the recombinant peptides flanked by CFP and YFP. Finally, we report on our progress to develop highly folded 24mer and 28mer &bgr;-hairpin peptides through the use of fluorescence-based library screening.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".