Stimulation of Ca<sup>2+</sup>‐ATPase Transport Activity by a Small‐Molecule Drug
Bibliographic record
Abstract
Abstract The sarco(endo)plasmic reticulum Ca2+−ATPase (SERCA) hydrolyzes ATP to transport Ca2+ from the cytoplasm to the sarcoplasmic reticulum (SR) lumen, thereby inducing muscle relaxation. Dysfunctional SERCA has been related to various diseases. The identification of small‐molecule drugs that can activate SERCA may offer a therapeutic approach to treat pathologies connected with SERCA malfunction. Herein, we propose a method to study the mechanism of interaction between SERCA and novel SERCA activators, i. e. CDN1163, using a solid supported membrane (SSM) biosensing approach. Native SR vesicles or reconstituted proteoliposomes containing SERCA were adsorbed on the SSM and activated by ATP concentration jumps. We observed that CDN1163 reversibly interacts with SERCA and enhances ATP‐dependent Ca2+ translocation. The concentration dependence of the CDN1163 effect provided an EC50=6.0±0.3 μM. CDN1163 was shown to act directly on SERCA and to exert its stimulatory effect under physiological Ca2+ concentrations. These results suggest that CDN1163 interaction with SERCA can promote a protein conformational state that favors Ca2+ release into the SR lumen.
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 | 0.000 |
| 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".