Sarcolambans are phospholamban- and sarcolipin-like regulators of the sarcoplasmic reticulum calcium pump SERCA
Bibliographic record
Abstract
ABSTRACT From insects to humans, calcium signaling is essential for life. An important part of this process is the sarco-endoplasmic reticulum calcium pump SERCA, which maintains low cytosolic calcium levels required for intracellular calcium homeostasis. In higher organisms, this is a tightly controlled system where SERCA interacts with tissuespecific regulatory subunits such as phospholamban in cardiac muscle and sarcolipin in skeletal muscle. With the recent discovery of the sarcolambans, the family of calcium pump regulatory subunits also appears to be ancient, spanning more than 550 million years of evolutionary divergence from insects to humans. This evolutionary divergence is reflected in the peptide sequences, which vary enormously from one another and range from vaguely phospholamban-like to vaguely sarcolipin-like. Here, our goal was to investigate select sarcolamban peptides for their ability to regulate calcium pump activity. For a side-by-side comparison of diverse sarcolamban peptides, we tested them against mammalian skeletal muscle SERCA1a. This allowed us to determine if the sarcolamban peptides mimic phospholamban and sarcolipin in their regulatory activities. Four sarcolamban peptides were chosen from different invertebrate species. Of these, we were able to express and purify sarcolamban peptides from bumble bee, water flea, and tadpole shrimp. Sarcolamban peptides were co-reconstituted into proteoliposomes with mammalian SERCA1a and the effect of each peptide on the apparent calcium affinity and maximal activity of SERCA was measured. While all peptides were super-inhibitors of SERCA, they exhibited either phospholamban-like or sarcolipin-like characteristics. Molecular modeling, protein-protein docking, and molecular dynamics simulations were used to reveal novel features of insect versus mammalian calcium pumps and the sarcolamban regulatory subunits.
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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".