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Record W2330826300 · doi:10.1021/nl502665n

A Label-Free Untethered Approach to Single-Molecule Protein Binding Kinetics

2014· article· en· W2330826300 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNano Letters · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Interaction Studies and Fluorescence Analysis
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKineticsChemistryMoleculeReceptor–ligand kineticsHuman serum albuminOptical tweezersMolecular bindingBiophysicsPlasma protein bindingBiochemistryOrganic chemistryPhysicsBiology

Abstract

fetched live from OpenAlex

Single molecule approaches provide rich real-time dynamics of molecular interactions that are not accessible to ensemble measurements. Previous single molecule studies have relied on labeling and tethering, which alters the natural state of the protein. Here we use the double-nanohole (DNH) optical tweezer approach to measure protein binding kinetics at the single molecule level in a label-free, free-solution (untethered) way. The binding kinetics of human serum albumin (HSA) to tolbutamide and to phenytoin are in quantitative agreement with previous measurements, and our single-molecule approach reveals a biexponential behavior characteristic of a multistep process. The DNH optical tweezer is an inexpensive platform for studying the real-time binding kinetics of protein-small molecule interactions in a label-free, free-solution environment, which will be of interest to future studies including drug discovery.

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)0.000
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 score0.075
Threshold uncertainty score0.704

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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