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Record W2887884319 · doi:10.1039/c8ob01417e

Rational design of a highly reactive dicysteine peptide tag for fluorogenic protein labelling

2018· article· en· W2887884319 on OpenAlexafffund
Miroslava Strmiskova, Kelvin Tsao, Jeffrey W. Keillor

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsChemistryPeptideLabellingReactivity (psychology)HeLaCysteineMutantIntracellularHistoneRational designThiolCombinatorial chemistrySequence (biology)BiochemistryBiophysicsIn vitroEnzymeNanotechnology

Abstract

fetched live from OpenAlex

Rationally designed libraries of a short helical peptide sequence containing two cysteine residues were screened kinetically for their reactivity towards complementary dimaleimide fluorogens. This screening revealed variant sequences whose reactivity has been increased by an order of magnitude relative to the original sequence. The most reactive engineered sequences feature mutant residues bearing positive charges, suggesting the pKa values of the adjacent thiol groups have been significantly lowered, through electrostatic stabilization of the thiolate ionization state. pH-Rate profiles measured for several mutant sequences support this mechanism of rate enhancement. The practical utility of the enhanced reactivity of the final engineered dicysteine tag ('dC10*') was then demonstrated in the fluorogenic intracellular labelling of histone H2B in living HeLa cells.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.244
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2018
Admission routes2
Has abstractyes

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