Polar mutations in membrane proteins as a biophysical basis for disease
Bibliographic record
Abstract
Transmembrane (TM) alpha-helices are surrounded by the hydrocarbon chains of the lipid bilayer. The low dielectric constant of this environment makes it extremely unfavorable for a residue with a polar side chain to exist in a non-H-bonded state. Therefore, in combination with a wild-type polar residue partner, a polar TM mutant could generate, in some cases, a non-native H-bond that could impair native protein structure/function-and possibly lead to a disease state. We have examined protein mutation databases and have found many examples of TM-based apolar to polar mutations that are, in fact, a cause of human disease. Here we review the various molecular defects that such mutations can produce, including impeding protein dynamics by side-chain-side-chain interhelical H-bond cross-links; alteration of helical packing through steric hindrance; and disruption of a protein active site. We further note that the reverse case--membrane-embedded polar to apolar mutations--can similarly cause human disease, implying that native interhelical H-bonds can also play pivotal roles in stabilizing native TM domains. As a specific example, we show that the Gly to Arg mutation occurs statistically more frequently in TM domains as compared to its occurrence in soluble domains, suggesting that TM-based G-to-R mutations have a high "phenotypic propensity" for disease. A more complete understanding of how mutations involving polar residues in TM domains of proteins translate into compromised function may aid in the development of novel therapeutics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".