Sarcolipin regulates sarco(endo)plasmic reticulum Ca <sup>2+</sup> -ATPase (SERCA) by binding to transmembrane helices alone or in association with phospholamban
Bibliographic record
Abstract
Phospholamban (PLN), a regulator of sarco(endo)plasmic reticulum Ca(2+)-ATPases (SERCAs), interacts with both the cytosolic N domain and transmembrane helices M2, M4, M6, and M9 of SERCA. Amino acids in the transmembrane domain of PLN that are predicted to interact with SERCA1a are conserved in sarcolipin (SLN), a functional PLN homologue. Accordingly, the effects of critical mutations in SERCA1a, PLN, and NF-SLN (SLN tagged N-terminally with a FLAG epitope) on NF-SLN/SERCA1a and PLN/NF-SLN/SERCA1a interactions were compared. Critical mutations in SERCA1a and NF-SLN diminished functional interactions between SERCA1a and NF-SLN, indicating that NF-SLN and PLN interact with some of the same amino acids in SERCA1a. Mutations in PLN or NF-SLN affected the amount of SERCA1a that was coimmunoprecipitated in each complex with antibodies against either PLN or SLN, but not the pattern of coimmunoprecipitation. PLN mutations had more dramatic effects on SERCA1a coimmunoprecipitation than SLN mutations, suggesting that PLN dominates in the primary interaction with SERCA1a. Coimmunoprecipitation also confirmed that PLN and NF-SLN form a heterodimer that interacts with SERCA1a in a regulatory fashion to form a very stable PLN/NF-SLN/SERCA1a complex. Modeling showed that the SLN/SERCA1a complex closely resembles the PLN/SERCA1a complex, but with the luminal end of SLN extending to the loop connecting M1 and M2, where Tyr-29 and Tyr-31 interact with aromatic residues in SERCA1a. Modeling of the PLN/SLN/SERCA1a complex predicts that the regulator binding cavity in the E(2) conformation of SERCA1a can accommodate both SLN and PLN helices, but not two PLN helices.
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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".