Missense mutations in transmembrane domains of proteins: Phenotypic propensity of polar residues for human disease
Bibliographic record
Abstract
Previous experiments on the cystic fibrosis transmembrane conductance regulator suggested that non-native polar residues within membrane domains can compromise protein structure/function. However, depending on context, replacement of a native residue by a non-native residue can result either in genetic disease or in benign effects (e.g., polymorphisms). Knowledge of missense mutations that frequently cause protein malfunction and subsequent disease can accordingly reveal information as to the impact of these residues in local protein environments. We exploited this concept by performing a statistical comparison of disease-causing mutations in protein membrane-spanning domains versus soluble domains. Using the Human Gene Mutation Database of 240 proteins (including 80 membrane proteins) associated with human disease, we compared the relative phenotypic propensity to cause disease of the 20 naturally occurring amino acids when removed from-or inserted into-native protein sequences. We found that in transmembrane domains (TMDs), mutations involving polar residues, and ionizable residues in particular (notably arginine), are more often associated with protein malfunction than soluble proteins. To further test the hypothesis that interhelical cross-links formed by membrane-embedded polar residues stabilize TMDs, we compared the occurrence of such residues in the TMDs of mesophilic and thermophilic prokaryotes. Results showed a significantly higher proportion of ionizable residues in thermophilic organisms, reinforcing the notion that membrane-embedded electrostatic interactions play critical roles in TMD stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".