Nothing Regular about the Regulins: Distinct Functional Properties of SERCA Transmembrane Peptide Regulatory Subunits
Bibliographic record
Abstract
The sarco-endoplasmic reticulum calcium ATPase (SERCA) is responsible for maintaining calcium homeostasis in all eukaryotic cells by actively transporting calcium from the cytosol into the sarco-endoplasmic reticulum (SR/ER) lumen. Calcium is an important signaling ion, and the activity of SERCA is critical for a variety of cellular processes such as muscle contraction, neuronal activity, and energy metabolism. SERCA is regulated by several small transmembrane peptide subunits that are collectively known as the "regulins". Phospholamban (PLN) and sarcolipin (SLN) are the original and most extensively studied members of the regulin family. PLN and SLN inhibit the calcium transport properties of SERCA and they are required for the proper functioning of cardiac and skeletal muscles, respectively. Myoregulin (MLN), dwarf open reading frame (DWORF), endoregulin (ELN), and another-regulin (ALN) are newly discovered tissue-specific regulators of SERCA. Herein, we compare the functional properties of the regulin family of SERCA transmembrane peptide subunits and consider their regulatory mechanisms in the context of the physiological and pathophysiological roles of these peptides. We present new functional data for human MLN, ELN, and ALN, demonstrating that they are inhibitors of SERCA with distinct functional consequences. Molecular modeling and molecular dynamics simulations of SERCA in complex with the transmembrane domains of MLN and ALN provide insights into how differential binding to the so-called inhibitory groove of SERCA-formed by transmembrane helices M2, M6, and M9-can result in distinct functional outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".