Structure dissection of human progranulin identifies well‐folded granulin/epithelin modules with unique functional activities
Bibliographic record
Abstract
Progranulin is a secreted protein with important functions in several physiological and pathological processes, such as embryonic development, host defense, and wound repair. Autosomal dominant mutations in the progranulin gene cause frontotemporal dementia, while overexpression of progranulin promotes the invasive progression of a range of tumors, including those of the breast and the brain. Structurally, progranulin consists of seven-and-a-half tandem repeats of the granulin/epithelin module (GEM), several of which have been isolated as discrete 6-kDa GEM peptides. We have expressed all seven human GEMs using recombinant DNA in Escherichia coli. High-resolution NMR showed that only the three GEMs, hGrnA, hGrnC, and hGrnF, contain relatively well-defined three-dimensional structures in solution, while others are mainly mixtures of poorly structured disulfide isomers. The three-dimensional structures of hGrnA, hGrnC, and hGrnF contain a stable stack of two beta-hairpins in their N-terminal subdomains, but showed a more flexible C-terminal subdomain. Interestingly, of the well-structured GEMs, hGrnA demonstrated potent growth inhibition of a breast cancer cell line, while hGrnF was stimulatory. Poorly folded peptides were either weakly inhibitory or without activity. The functionally active and structurally well-characterized human hGrnA offers a unique opportunity for detailed structure-function studies of these important GEM proteins as novel members of mammalian growth factors.
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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) | 0.000 | 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".