Cardiac RyR N-terminal region biosensors for FRET-based high-throughput screening
Bibliographic record
Abstract
Abstract The N-terminal region (NTR) of the ryanodine receptor (RyR) calcium channels is critical to the regulation of Ca 2+ release during excitation-contraction coupling. NTR hosts numerous mutations linked to skeletal and cardiac myopathies (RyR1 and RyR2, respectively), highlighting its potential as therapeutic target. Here, we labeled the NTR of mouse RyR2 at subdomains A, B, and C with donor and acceptor pairs for fluorescence resonance energy transfer (FRET), obtaining two biosensors. Using fluorescence lifetime (FLT)-detection of intramolecular FRET, we developed high-throughput screening (HTS) assays with the biosensors to identify small-molecule modulators of RyR. We screened a 1280-compound validation library and identified several hits. Hits with saturable FRET dose-response profiles, and previously unreported effects on RyR activity, were further tested using [ 3 H]ryanodine binding to isolated sarcoplasmic reticulum vesicles, to measure their effects on full-length RyR opening in its natural membrane environment. We identified three novel inhibitors of both RyR1 and RyR2, and two RyR1-selective inhibitors at nanomolar Ca 2+ . These compounds may function as inhibitors of leaky RyRs in muscle. Two of these hits activated RyR1 only at micromolar Ca 2+ , highlighting them as potential activators of excitation-contraction coupling. These results indicate that large-scale HTS using this platform can lead to compounds with potential for therapeutic development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".