The Affinity of GXXXG Motifs in Transmembrane Helix-Helix Interactions Is Modulated by Long-range Communication
Bibliographic record
Abstract
Sequence motifs are responsible for ensuring the proper assembly of transmembrane (TM) helices in the lipid bilayer. To understand the mechanism by which the affinity of a common TM-TM interactive motif is controlled at the sequence level, we compared two well characterized GXXXG motif-containing homodimers, those formed by human erythrocyte protein glycophorin A (GpA, high-affinity dimer) and those formed by bacteriophage M13 major coat protein (MCP, low affinity dimer). In both constructs, the GXXXG motif is necessary for TM-TM association. Although the remaining interfacial residues (underlined) in GpA (LIXXGVXXGVXXT) differ from those in MCP (VVXXGAXXGIXXF), molecular modeling performed here indicated that GpA and MCP dimers possess the same overall fold. Thus, we could introduce GpA interfacial residues, alone and in combination, into the MCP sequence to help decrypt the determinants of dimer affinity. Using both in vivo TOXCAT assays and SDS-PAGE gel migration rates of synthetic peptides derived from TM regions of the proteins, we found that the most distal interfacial sites, 12 residues apart (and approximately 18 A in structural space), work in concert to control TM-TM affinity synergistically.
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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".