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Record W2085714603 · doi:10.1039/c1an15235a

Modified peptide monolayer binding His-tagged biomolecules for small ligand screening with SPR biosensors

2011· article· en· W2085714603 on OpenAlexaff
Olivier R. Bolduc, Patrick Lambert-Lanteigne, Damien Y. Colin, Sandy Shuo Zhao, Caroline Proulx, Damien Boeglin, William D. Lubell, Joelle N. Pelletier, James Féthière, Huy Ong, Jean‐François Masson

Bibliographic record

VenueThe Analyst · 2011
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsInstitute for Research in Immunology and CancerPROTEOUniversité de Montréal
Fundersnot available
KeywordsMonolayerChemistryPeptideSurface plasmon resonanceBiomoleculeBiosensorLigand (biochemistry)ChelationCombinatorial chemistryBiochemistryNanotechnologyReceptorOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

A peptide self-assembled monolayer (SAM) was designed to bind His-tagged biomolecules for surface plasmon resonance (SPR) bioanalysis, which was applied for the determination of K(d) for small ligand screening against CD36. Nonspecific adsorption could be minimized using penta- and hexa-peptide monolayers. In particular, monolayers consisting of 3-mercaptopropionyl-leucinyl-histidinyl-aspartyl-leucinyl-histidinyl-aspartic acid (3-Mpa-LHDLHD) exhibited little (12 ng cm(-2)) nonspecific adsorption in crude serum. Modification of this peptide monolayer with Nα,Nα-bis(carboxymethyl)-L-lysine gave a surface competent for binding His-tagged proteins, as demonstrated using enzyme (human dihydrofolate reductase), protein/antibody and receptor (CD36) examples. Immobilization featured chelation of copper and the His-tagged protein by the peptide monolayer, which could be recycled by removing the copper using imidazole washes prior to reuse.

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 score0.101
Threshold uncertainty score0.469

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 head0.030
GPT teacher head0.203
Teacher spread0.173 · 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

Citations51
Published2011
Admission routes1
Has abstractyes

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