Phosphorylation-dependent Changes in Nucleotide Binding, Conformation, and Dynamics of the First Nucleotide Binding Domain (NBD1) of the Sulfonylurea Receptor 2B (SUR2B)
Bibliographic record
Abstract
The sulfonylurea receptor 2B (SUR2B) forms the regulatory subunit of ATP-sensitive potassium (KATP) channels in vascular smooth muscle. Phosphorylation of the SUR2B nucleotide binding domains (NBD1 and NBD2) by protein kinase A results in increased channel open probability. Here, we investigate the effects of phosphorylation on the structure and nucleotide binding properties of NBD1. Phosphorylation sites in SUR2B NBD1 are located in an N-terminal tail that is disordered. Nuclear magnetic resonance (NMR) data indicate that phosphorylation of the N-terminal tail affects multiple residues in NBD1, including residues in the NBD2-binding site, and results in altered conformation and dynamics of NBD1. NMR spectra of NBD1 lacking the N-terminal tail, NBD1-ΔN, suggest that phosphorylation disrupts interactions of the N-terminal tail with the core of NBD1, a model supported by dynamic light scattering. Increased nucleotide binding of phosphorylated NBD1 and NBD1-ΔN, compared with non-phosphorylated NBD1, suggests that by disrupting the interaction of the NBD core with the N-terminal tail, phosphorylation also exposes the MgATP-binding site on NBD1. These data provide insights into the molecular basis by which phosphorylation of SUR2B NBD1 activates KATP channels.
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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".