Distinctions between Hydrophobic Helices in Globular Proteins and Transmembrane Segments as Factors in Protein Sorting
Bibliographic record
Abstract
Transmembrane (TM) segments in proteins can be distinguished in amino acid sequences as continuous stretches of hydrophobic residues. However, examination of a data base of helical water-soluble (globular) proteins revealed that nearly one-third contained helices of sufficient length to span a bilayer (> or =19 residues) that had mean hydrophobicity > or =actual TM segments. We now report that synthetic peptides corresponding to these globular protein sequences, which we termed "delta-helices," behave like native TM sequences and readily insert into membrane mimetic environments in helical conformations. As well, certain delta-helix sequences can integrate into the membrane bilayer when placed into a membrane-targeted chimeric protein. We establish that delta-helices can be distinguished computationally from bona fide TM segments by the decreased frequency of occurrence of Ile/Val residues and by their relatively decreased solvent accessibilities (versus other globular helices) within tertiary structure. The further observations that (i) delta-helices generally contain three or more charged residues and (ii) delta-helices display relatively even distribution of these charged residues along their lengths, rather than concentration near their N and C termini as observed for TM segments, may constitute key recognition factors in diverting delta-helices from the membrane in vivo. Although a discrete biological role for delta-helices remains to be pinpointed, our overall results suggest that such segments may be required for globular protein folding and identify additional factors that may be important in the correct selection of TM segments by the cellular machinery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".