Oligomeric Interactions of Sarcolipin and the Ca-ATPase
Bibliographic record
Abstract
We have detected directly the interactions of sarcolipin (SLN) and the sarcoplasmic reticulum Ca-ATPase (SERCA) by measuring fluorescence resonance energy transfer (FRET) between fusion proteins labeled with cyan fluorescent protein (donor) and yellow fluorescent protein (acceptor). SLN is a membrane protein that helps control contractility by regulating SERCA activity in fast-twitch and atrial muscle. Here we used FRET microscopy and spectroscopy with baculovirus expression in insect cells to provide direct evidence for: 1) oligomerization of SLN and 2) regulatory complex formation between SLN and the fast-twitch muscle Ca-ATPase (SERCA1a isoform). FRET experiments demonstrated that SLN monomers self-associate into dimers and higher order oligomers in the absence of SERCA, and that SLN monomers also bind to SERCA monomers in a 1:1 binary complex when the two proteins are coexpressed. FRET experiments further demonstrated that the binding affinity of SLN for itself is similar to that for SERCA. Mutating SLN residue isoleucine-17 to alanine (I17A) decreased the binding affinity of SLN self-association and converted higher order oligomers into monomers and dimers. The I17A mutation also decreased SLN binding affinity for SERCA but maintained 1:1 stoichiometry in the regulatory complex. Thus, isoleucine-17 plays dual roles in determining the distribution of SLN homo-oligomers and stabilizing the formation of SERCA-SLN heterodimers. FRET results for SLN self-association were supported by the effects of SLN expression in bacterial cells. We propose that SLN exists as multiple molecular species in muscle, including SERCA-free (monomer, dimer, oligomer) and SERCA-bound (heterodimer), with transmembrane zipper residues of SLN serving to stabilize oligomeric interactions.
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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 | 0.001 |
| 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".