Sarco(endo)plasmic Reticulum Calcium ATPase (SERCA) Inhibition by Sarcolipin Is Encoded in Its Luminal Tail
Bibliographic record
Abstract
The sarco(endo)plasmic reticulum calcium ATPase (SERCA) is regulated in a tissue-dependent manner via interaction with the short integral membrane proteins phospholamban (PLN) and sarcolipin (SLN). Although defects in SERCA activity are known to cause heart failure, the regulatory mechanisms imposed by PLN and SLN could have clinical implications for both heart and skeletal muscle diseases. PLN and SLN have significant sequence homology in their transmembrane regions, suggesting a similar mode of binding to SERCA. However, unlike PLN, SLN has a conserved C-terminal luminal tail composed of five amino acids ((27)RSYQY), which may contribute to a distinct SERCA regulatory mechanism. We have functionally characterized alanine mutants of the C-terminal tail of SLN using co-reconstituted proteoliposomes of SERCA and SLN. We found that Arg(27) and Tyr(31) are essential for SLN function. We also tested the effect of a truncated variant of SLN (Arg(27)stop) and extended chimeras of PLN with the five luminal residues of SLN added to its C terminus. The Arg(27)stop form of SLN resulted in loss of function, whereas the PLN chimeras resulted in superinhibition with characteristics of both PLN and SLN. Based on our results, we propose that the C-terminal tail of SLN is a distinct, essential domain in the regulation of SERCA and that the functional properties of the SLN tail can be transferred to PLN.
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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".