A Hydrophobic Patch in a Charged α-Helix Is Sufficient to Target Proteins to Dense Core Secretory Granules
Bibliographic record
Abstract
Many endocrine and neuroendocrine cells contain specialized secretory organelles called dense core secretory granules. These organelles are the repository of proteins and peptides that are secreted in a regulated manner when the cell receives a physiological stimulus. The targeting of proteins to these secretory granules is crucial for the generation of certain peptide hormones, including insulin and ACTH. Although previous work has demonstrated that proteins destined to a variety of cellular locations, including secretory granules, contain targeting sequences, no single consensus sequence for secretory granule-sorting signals has emerged. We have shown previously that alpha-helical domains in the C-terminal tail of the prohormone convertase PC1/3 play an important role in the ability of this region of the protein to direct secretory granule targeting (Jutras, I. Seidah, N. G., and Reudelhuber, T. L. (2000) J. Biol. Chem. 275, 40337-40343). In this study, we show that a variety of alpha-helical domains are capable of directing a heterologous secretory protein to granules. By testing a series of synthetic alpha-helices, we also demonstrate that the presence of charged (either positive or negative) amino acids spatially segregated from a hydrophobic patch in the alpha-helices of secretory proteins likely plays a critical role in the ability of these structures to direct secretory granule sorting.
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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) | 0.001 | 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".