Nonpolar contributions to conformational specificity in assemblies of designed short helical peptides
Bibliographic record
Abstract
A series of designed short helical peptides was used to study the effect of nonpolar interactions on conformational specificity. The consensus sequence was designed to obtain short helices (17 residues) and to minimize the presence of interhelical polar interactions. Furthermore, the sequence contained a heptad repeat (abcdefg), where positions a and d were occupied by hydrophobic residues Leu, Ile, or Val, and positions e and g were occupied by Ala. The peptides were named according to the identities of the residues in the adeg positions, respectively. The peptides llaa, liaa, ilaa, iiaa, ivaa, viaa, lvaa, vlaa, and vvaa were synthesized, and their characterization revealed marked differences in specificity. An experimental methodology was developed to study the nine peptides and their pairwise mixtures. These peptides and their mixtures formed a vast array of structural states, which may be classified as follows: helical tetramers and pentamers, soluble and insoluble helical aggregates, insoluble unstructured aggregates, and soluble unstructured monomers. The peptide liaa formed stable helical pentamers, and iiaa and vlaa formed stable helical tetramers. Disulfide cross-linking experiments indicated the presence of an antiparallel helix alignment in the helical pentamers and tetramers. Rates of amide proton exchange of the tetrameric form of vlaa were 10-fold slower than the calculated exchange rate for unfolded vlaa. In other work, the control of specificity has been attributed to polar interactions, especially buried polar interactions; this work demonstrated that subtle changes in the configuration of nonpolar interactions resulted in a large variation in the extent of conformational specificity of assemblies of designed short helical peptides. Thus, nonpolar interactions can have a significant effect on the conformational specificity of oligomeric short helices.
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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".