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Record W2015709915 · doi:10.1039/b912083a

Electrochemical probing of HIV enzymes using ferrocene-conjugated peptides on surfaces

2009· article· en· W2015709915 on OpenAlexafffund
Kağan Kerman, Heinz‐Bernhard Kraatz

Bibliographic record

VenueThe Analyst · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsWestern University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryEnzymeConjugated systemPeptideProteaseBiochemistryIntegraseCombinatorial chemistryDNAOrganic chemistry

Abstract

fetched live from OpenAlex

One of the current pathways to develop inhibitors that target different steps in the life cycle of the human immunodeficiency virus (HIV) is blocking the function of the HIV-related proteins such as HIV-1 integrase (HIV-1 IN), HIV-1 reverse transcriptase (HIV-1 RT) and HIV-1 protease (HIV-1 PR), which are essential proteins that control the ability of HIV to infect cells, produce new copies of the virus, or cause disease. We have demonstrated for the first time the detection of these enzymes at nanomolar levels using ferrocene (Fc)-conjugated peptides on gold microelectrodes. The interaction between the Fc-conjugated peptides and the enzymes was studied by cyclic voltammetry. As the protein concentration increased, the electrochemical behaviour of the surface-bound Fc- bioconjugate changed, indicating that HIV protein was binding to the peptide film and encapsulating the Fc redox center on the surface. The electrochemical responses shifted to higher potentials and decreased in the current intensity, as the concentrations of the HIV-1 enzymes increased. The optimization studies were performed by changing the pH and NaCl concentration. Control experiments involved the exposure of the Fc-conjugated peptides with all the enzymes. This general procedure can be readily applied in the future to the multiplexed detection of several HIV-related proteins, as well as the high-throughput screening of candidate inhibitors for AIDS therapy.

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 score0.001
Threshold uncertainty score0.390

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations41
Published2009
Admission routes2
Has abstractyes

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