Peptides as transmembrane segments: Decrypting the determinants for helix–helix interactions in membrane proteins
Bibliographic record
Abstract
Abstract Although the structural analysis of membrane proteins is advancing, an understanding of the basic principles that underlie their folding and assembly remains limited because of the high insolubility intrinsic to these molecules and concomitant challenges in obtaining crystals. Fortunately, from an experimental standpoint, membrane protein folding can be approximated as the rigid‐body docking of pre‐formed α‐helical transmembrane segments one with another to form the final functional protein structure. Peptides derived from the sequences of native α‐helical transmembrane segments and those that mimic their properties are therefore valuable in the experimental evaluation of protein folding within the membrane. Here we present an overview of the progress made in our laboratory and elsewhere in using peptide models toward defining the sequence requirements and forces stabilizing membrane protein folds. © 2007 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 88: 217–232, 2007. This article was originally published online as an accepted preprint. The ‘Published Online’ date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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 | 0.001 |
| 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".