Polar residue tagging of transmembrane peptides
Bibliographic record
Abstract
Studies that focus on packing interactions between transmembrane (TM) helices in membrane proteins would greatly benefit from the ability to investigate their association and packing interactions in multi-spanning TM domains. However, the production, purification, and characterization of such units have been impeded by their high intrinsic hydrophobicity. We describe the polar tagging approach to biophysical analysis of TM segment peptides, where incorporation of polar residues of suitable type and number at one or both peptide N- and C-termini can serve to counterbalance the apolar nature of a native TM segment, and render it aqueous-soluble. Using the native TM sequences of the human erythrocyte protein glycophorin A (GpA) and bacteriophage M13 major coat protein (MCP), properties of tags such as Lys, His, Asp, sarcosine, and Pro-Gly are evaluated, and general procedures for tagging a given TM segment are presented. Gel-shift assays on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) establish that various tagged GpA TM segments spontaneously insert into micellar membranes, and exhibit native TM dimeric states. Sedimentation equilibrium analytical centrifugation is used to confirm that Lys-tagged GpA peptides retain the native dimer state. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy studies on Lys-tagged TM MCP peptides selectively enriched with N-15 illustrate the usefulness of this system for evaluating monomer-dimer equilibria in micelle environments. The overall results suggest that polar-tagging of hydrophobic (TM) peptides approach constitutes a valuable tool for the study of protein-protein interactions in membranes.
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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) | 0.001 | 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".