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Record W4383825069 · doi:10.1039/d3ay00512g

Tri-part NanoLuc as a new split technology with potential applications in chemical biology: a mini-review

2023· review· en· W4383825069 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnalytical Methods · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicbioluminescence and chemiluminescence research
Canadian institutionsUniversity of Manitoba
FundersIran National Science Foundation
KeywordsChemistryCRISPRCleaveChemical biologyComputational biologyNanotechnologyBiologyBiochemistryEnzymeMaterials science

Abstract

fetched live from OpenAlex

, has been found to have many benefits over other luminescent enzymes in PCA. Inspired by the split green fluorescent protein (GFP) and its β-barrel structure, two split NLuc consisting of peptide fragments have been reported including the binary and ternary NLuc systems. NanoBiT® (large fragment + peptide) has been used extensively. In contrast, tripart split NLuc (large fragment + 2 peptides) has been applied and hardly used, while it has some advantages over NanoBiT in some studies. Nevertheless, tripart NLuc has some drawbacks and challenges to overcome but has several potential characteristics to become a multifunctional and powerful tool. In this review, several aspects of tripart NLuc are studied and a brief comparison with NanoBiT® is given.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.073
GPT teacher head0.479
Teacher spread0.406 · 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