Clustering of Large Hydrophobes in the Hydrophobic Core of Two-stranded α-Helical Coiled-Coils Controls Protein Folding and Stability
Bibliographic record
Abstract
The de novo design and biophysical characterization of two 60-residue peptides that dimerize to fold as parallel coiled-coils with different hydrophobic core clustering is described. Our goal was to investigate whether designing coiled-coils with identical hydrophobicity but with different hydrophobic clustering of non-polar core residues (each contained 6 Leu, 3 Ile, and 7 Ala residues in the hydrophobic core) would affect helical content and protein stability. The disulfide-bridged P3 and P2 differed dramatically in alpha-helical structure in benign conditions. P3 with three hydrophobic clusters was 98% alpha-helical, whereas P2 was only 65% alpha-helical. The stability profiles of these two analogs were compared, and the enthalpy and heat capacity changes upon denaturation were determined by measuring the temperature dependence by circular dichroism spectroscopy and confirmed by differential scanning calorimetry. The results showed that P3 assembled into a stable alpha-helical two-stranded coiled-coil and exhibited a native protein-like cooperative two-state transition in thermal melting, chemical denaturation, and calorimetry experiments. Although both peptides have identical inherent hydrophobicity (the hydrophobic burial of identical non-polar residues in equivalent heptad coiled-coil positions), we found that the context dependence of an additional hydrophobic cluster dramatically increased stability of P3 (Delta Tm approximately equal to 18 degrees C and Delta[urea](1/2) approximately equal to 1.5 M) as compared with P2. These results suggested that hydrophobic clustering significantly stabilized the coiled-coil structure and may explain how long fibrous proteins like tropomyosin maintain chain integrity while accommodating polar or charged residues in regions of the protein hydrophobic core.
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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".