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Record W2042535394 · doi:10.1021/ja0481124

Sweeping Capillary Electrophoresis:  A Non-Stopped-Flow Method for Measuring Bimolecular Rate Constant of Complex Formation between Protein and DNA

2004· article· en· W2042535394 on OpenAlexaff
Victor Okhonin, Maxim V. Berezovski, Sergey N. Krylov

Bibliographic record

VenueJournal of the American Chemical Society · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsYork University
Fundersnot available
KeywordsChemistryCapillary electrophoresisDNAElectrophoresisOligonucleotideCapillary actionGel electrophoresis of nucleic acidsReaction rate constantA-DNAKineticsChromatographyBiophysicsAnalytical Chemistry (journal)ThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

We introduce sweeping capillary electrophoresis (SweepCE), a non-stopped-flow method for directly measuring the bimolecular rate constant of complex formation, and demonstrate its use for studying protein-DNA interaction. The capillary is prefilled with a solution of DNA, and electrophoresis is then carried out from a solution of the protein in a continuous mode. Because the electrophoretic mobility of the protein is greater than that of DNA, the protein continuously mixes with DNA and forms the protein-DNA complex. The complex migrates with a velocity higher than that of DNA and causes sweeping of DNA, which gave the name to the method. The bimolecular rate constant, kon, of complex formation can be determined from the time profile of DNA concentration using a simple mathematical model of the sweeping process. In this proof-of-principle work, we used SweepCE to directly measure kon = (3.4 +/- 0.6) x 106 M-1 s-1 for the interaction between single-stranded DNA-binding protein and a 15-mer DNA oligonucleotide. Along with nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), SweepCE establishes a universal and comprehensive platform for studying kinetic and equilibrium parameters of complex formation between biopolymers.

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)0.000
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 score0.177
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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

Citations39
Published2004
Admission routes1
Has abstractyes

Explore more

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