Modulating Transmembrane α-Helix Interactions through pH-Sensitive Boundary Residues
Bibliographic record
Abstract
Changes in pH can alter the structure and activity of proteins and may be used by the cell to control molecular function. This coupling can also be used in non-native applications through the design of pH-sensitive biomolecules. For example, the pH (low) insertion peptide (pHLIP) can spontaneously insert into a lipid bilayer when the pH decreases. We have previously shown that the α-helicity and helix-helix interactions of the TM2 α-helix of the proteolipid protein (PLP) are sensitive to the local hydrophobicity at its C-terminus. Given that there is an ionizable residue (Glu-88) at the C-terminus of this transmembrane (TM) segment, we hypothesized that changing the ionization state of this residue through pH may alter the local hydrophobicity of the peptide enough to affect both its secondary structure and helix-helix interactions. To examine this phenomenon, we synthesized peptide analogues of the PLP TM2 α-helix (wild-type sequence (66)AFQYVIYGTASFFFLYGALLLAEGF(90)). Using circular dichroism and Förster resonance energy transfer in the membrane-mimetic detergent sodium dodecyl sulfate, we found that a decrease in pH increases both peptide α-helicity and the extent of self-association. This pH-dependent effect is due specifically to the presence of Glu-88 at the C-terminus. Additional experiments in which Phe-90 was mutated to residues of varying hydrophobicities indicated that the strength of this effect is dependent on the local hydrophobicity near Glu-88. Our results have implications for the design of TM peptide switches and improve our understanding of how membrane protein structure and activity can be regulated through local molecular environmental changes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".