Conformational memory in the association of the transmembrane protein phospholamban with the sarcoplasmic reticulum calcium pump SERCA
Bibliographic record
Abstract
-ATPase SERCA promotes muscle relaxation by pumping calcium ions from the cytoplasm into the sarcoplasmic reticulum. SERCA activity is regulated by a variety of small transmembrane peptides, most notably by phospholamban in cardiac muscle and sarcolipin in skeletal muscle. However, how phospholamban and sarcolipin regulate SERCA is not fully understood. In the present study, we evaluated the effects of phospholamban and sarcolipin on calcium translocation and ATP hydrolysis by SERCA under conditions that mimic environments in sarcoplasmic reticulum membranes. For pre-steady-state current measurements, proteoliposomes containing SERCA and phospholamban or sarcolipin were adsorbed to a solid-supported membrane and activated by substrate concentration jumps. We observed that phospholamban altered ATP-dependent calcium translocation by SERCA within the first transport cycle, whereas sarcolipin did not. Using pre-steady-state charge (calcium) translocation and steady-state ATPase activity under substrate conditions (various calcium and/or ATP concentrations) promoting particular conformational states of SERCA, we found that the effect of phospholamban on SERCA depends on substrate preincubation conditions. Our results also indicated that phospholamban can establish an inhibitory interaction with multiple SERCA conformational states with distinct effects on SERCA's kinetic properties. Moreover, we noted multiple modes of interaction between SERCA and phospholamban and observed that once a particular mode of association is engaged it persists throughout the SERCA transport cycle and multiple turnover events. These observations are consistent with conformational memory in the interaction between SERCA and phospholamban, thus providing insights into the physiological role of phospholamban and its regulatory effect on SERCA transport activity.
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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 | 0.001 |
| 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".