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Record W3118694584 · doi:10.3390/ijms22010445

Switching between Ultrafast Pathways Enables a Green-Red Emission Ratiometric Fluorescent-Protein-Based Ca2+ Biosensor

2021· article· en· W3118694584 on OpenAlexafffund
Longteng Tang, Shuce Zhang, Y Zhao, Nikita D. Rozanov, Liangdong Zhu, Jiahui Wu, Robert E. Campbell, Chong Fang

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversity of Alberta
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Science Foundation
KeywordsBiosensorFörster resonance energy transferFluorescenceChromophorePhotoexcitationChemistryExcited stateElectron transferPhotochemistryNanotechnologyMaterials scienceBiochemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Ratiometric indicators with long emission wavelengths are highly preferred in modern bioimaging and life sciences. Herein, we elucidated the working mechanism of a standalone red fluorescent protein (FP)-based Ca2+ biosensor, REX-GECO1, using a series of spectroscopic and computational methods. Upon 480 nm photoexcitation, the Ca2+-free biosensor chromophore becomes trapped in an excited dark state. Binding with Ca2+ switches the route to ultrafast excited-state proton transfer through a short hydrogen bond to an adjacent Glu80 residue, which is key for the biosensor’s functionality. Inspired by the 2D-fluorescence map, REX-GECO1 for Ca2+ imaging in the ionomycin-treated human HeLa cells was achieved for the first time with a red/green emission ratio change (ΔR/R0) of ~300%, outperforming many FRET- and single FP-based indicators. These spectroscopy-driven discoveries enable targeted design for the next-generation biosensors with larger dynamic range and longer emission wavelengths.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.294
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2021
Admission routes2
Has abstractyes

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