Fully reduced granulin-B is intrinsically disordered and displays concentration-dependent dynamics
Bibliographic record
Abstract
Granulins (Grns) are a family of small, cysteine-rich proteins that are generated upon proteolytic cleavage of their precursor, progranulin (Pgrn). All seven Grns (A-G) contain 12 conserved cysteines that form 6 intramolecular disulfide bonds, rendering this family of proteins unique. Grns are known to play multi-functional roles, including wound healing, embryonic growth, and inflammation and are implicated in neurodegenerative diseases. Despite their manifold functions, there exists a dearth of information regarding their structure-function relationship. Here, we sought to establish the role of disulfide bonds in promoting structure by investigating the fully reduced GrnB (rGrnB). We report that monomeric rGrnB is an intrinsically disordered protein (IDP) at low concentrations. rGrnB undergoes dimerization at higher concentrations to form a fuzzy complex without a net gain in the structure-a behavior increasingly identified as a hallmark of some IDPs. Interestingly, we show that rGrnB is also able to activate NF-κB in human neuroblastoma cells in a concentration-dependent manner. This activation correlates with the observed monomer-dimer dynamics. Collectively, the presented data establish that the intrinsic disorder of rGrnB governs conformational dynamics within the reduced form of the protein, and suggest that the overall structure of Grns could be entirely dictated by disulfide bonds.
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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".