Acrylamide concentration determines the direction and magnitude of helical membrane protein gel shifts
Bibliographic record
Abstract
SDS/PAGE is universally used in biochemistry, cell biology, and immunology to resolve minute protein amounts readily from tissue and cell extracts. Although molecular weights of water-soluble proteins are reliably determined from their SDS/PAGE mobility, most helical membrane proteins, which comprise 20-30% of the human genome and the majority of drug targets, migrate to positions that have for decades been unpredictably slower or faster than their actual formula weight, often confounding their identification. Using de novo designed transmembrane-mimetic polypeptides that match the composition of helical membrane-spanning sequences, we quantitate anomalous SDS/PAGE fractionation of helical membrane proteins by comparing the relative mobilities of these polypeptides with typical water-soluble reference proteins on Laemmli gels. We find that both the net charge and effective molecular size of the migrating particles of transmembrane-mimetic species exceed those of the corresponding reference proteins and that gel acrylamide concentration dictates the impact of these two factors on the direction and magnitude of anomalous migration. Algorithms we derived from these data compensate for this differential effect of acrylamide concentration on the SDS/PAGE mobility of a variety of natural membrane proteins. Our results provide a unique means to predict anomalous migration of membrane proteins, thereby facilitating straightforward determination of their molecular weights via SDS/PAGE.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".