Development of an miRFP680-Based Fluorescent Calcium Ion Biosensor Using End-Optimized Transposons
Bibliographic record
Abstract
The development of new or improved single fluorescent protein (FP)-based biosensors (SFPBs), particularly those with excitation and emission at near-infrared wavelengths, is important for the continued advancement of biological imaging applications. In an effort to accelerate the development of new SFPBs, we report modified transposons for the transposase-based creation of libraries of FPs randomly inserted into analyte binding domains, or vice versa. These modified transposons feature ends that are optimized to minimize the length of the linkers that connect the FP to the analyte binding domain. We rationalized that shorter linkers between the domains should result in more effective allosteric coupling between the analyte binding-dependent conformational change in the binding domain and the fluorescence modulation of the chromophore of the FP domain. As a proof of concept, we employed end-modified Mu transposons for the discovery of SFPB prototypes based on the insertion of two circularly permuted red FPs (mApple and FusionRed) into binding proteins for l -lactate and spermidine. Using an analogous approach, we discovered calcium ion (Ca 2+ )-specific SFPBs by random insertion of calmodulin (CaM)-RS20 into miRFP680, a particularly bright near-infrared (NIR) FP based on a biliverdin (BV)-binding fluorescent protein. Starting from an miRFP680-based Ca 2+ biosensor prototype, we performed extensive directed evolution, including under BV-deficient conditions, to create highly optimized biosensors designated the NIR-GECO3 series. We have extensively characterized the NIR-GECO3 series and explored their utility for biological Ca 2+ imaging. The methods described in this work will serve to accelerate SFPB development and open avenues for further exploration and optimization of SFPBs across a spectrum of biological applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".