Bipartite Genetically Encoded Biosensors to Sense Calcium Ion Dynamics at Membrane–Membrane Contact Sites
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Self-complementing bipartite fluorescent proteins (FPs) are useful tools for the detection of protein–protein proximity and for localizing fluorophores to membrane–membrane contact sites. Here, we report versions of circularly permuted green FP (GFP), red FP (RFP), and mNeonGreen (NG), which are split into a large fragment composed of nine β-strands and a small fragment composed of two β-strands. In each case, the large and small fragments can associate in live cells to form the complete 11-stranded FP β-barrel. We further converted each of these three self-complementing FPs into bipartite calcium ion (Ca 2+ ) biosensors. We demonstrate that appropriately targeted versions of these split FPs, and split FP-based biosensors, can be functionally assembled at membrane–membrane contact sites. We employ the bipartite NG-based Ca 2+ biosensor for visualization of pharmacologically induced Ca 2+ release at mitochondria-endoplasmic reticulum contact sites (MERCs).
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 | 0.001 |
| 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".