Design, expression, and purification of de novo transmembrane “hairpin” peptides
Bibliographic record
Abstract
While our understanding of the folding and structure of water-soluble proteins has progressed to the point where they can be artificially designed and produced from first principles, there has been only limited work toward the de novo design of membrane proteins. Such studies have been hindered in large part due to the practical challenges in the production and characterization of multispanning transmembrane (TM) proteins that arise from their highly hydrophobic character. In this work, we used molecular biology cloning techniques to produce a library of partially randomized Ala- and Ile-rich de novo helix-loop-helix (hairpin) TM constructs as models for tertiary TM-TM folding. From this plasmid DNA library, we selected sequences corresponding to hairpins with 0, 1, or 2 putative TM segments. While purification protocols could be adapted for application with a broad range of designed protein hairpins, bacterial expression of constructs with multiple predicted TM segments was limited as it is with native membrane proteins. Examples of the peptide hairpins obtained were characterized by circular dichroism spectroscopy, tryptophan fluorescence, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). We found that hairpins composed of two TM segments display characteristic behavior on detergent solubilization, such as an increase in helical structure (vs. that in aqueous buffer), and sequence-dependent migration rates in SDS-PAGE analysis-features that may serve as structural hallmarks to verify dual TM topology in hairpin sequences.
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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 | 0.000 |
| 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 | 0.001 |
| 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".