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Record W2067730243 · doi:10.1021/bc0503059

Synthesis of Acid-Cleavable Light Isotope-Coded Affinity Tags (ICAT-<i>L</i>) for Potential Use in Proteomic Expression Profiling Analysis

2005· article· en· W2067730243 on OpenAlexfundno aff
Abdul H. Fauq, Rajashaker Kache, Murad Ali Khan, Irving E. Vega

Bibliographic record

VenueBioconjugate Chemistry · 2005
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeCanadian Institute for Theoretical Astrophysics
KeywordsChemistryBiotinProteomeCombinatorial chemistryPeptideMoietyTandem mass spectrometryLinkerReagentCysteineTandem mass tagMass spectrometryChromatographyProteomicsBiochemistryQuantitative proteomicsStereochemistryEnzymeOrganic chemistry

Abstract

fetched live from OpenAlex

A convenient synthesis of some homologous light isotope-coded affinity tags (ICAT-L) containing an acid-labile moiety between the affinity component biotin and an electrophilic polar linker is described. These light ICAT reagents give smooth mass spectral signals in tandem mass spectrometry (MS/MS) analyses of some commercially available cysteine-containing peptides. However, these ICAT molecules are designed for use in identification and relative quantification of whole or partially purified cellular and tissue proteomes. Since the biotin moiety can be readily cleaved off the reagent after mass tagging, undesired residual fragmentation patterns caused by biotin of derived peptides, as normally observed using biotin-containing ICAT reagents, are effectively eliminated. This strategy should enhance peptide sequence coverage significantly which, in turn, should result in improving the quality of data obtained during data-dependent peptide mass and tandem mass spectral analysis of whole proteomes.

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 categoriesMeta-epidemiology (narrow)
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 score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.256
Teacher spread0.241 · 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.

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
Published2005
Admission routes1
Has abstractyes

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