MétaCan
Menu
Back to cohort
Record W2078264732 · doi:10.1039/c0an00003e

Peptide quantitation with methyl iodide isotopic tags and mass spectrometry

2010· article· en· W2078264732 on OpenAlexafffund
Voislav Blagojevic, Nickholas Zhidkov, Samuel Tharmaratnam, Van Thong Pham, Harvey Kaplan, Diethard K. Böhme

Bibliographic record

VenueThe Analyst · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsYork University
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMass spectrometryChemistryMethyl iodideChromatographyIodideInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A novel method is presented for the quantitation of peptides based on their methylation by in vacuo chemical reaction with methyl iodide. Samples of two small peptides, hexaglycine and pentaalanine, were labeled with CH(3)I and CD(3)I, representing the "unknown" and "standard" respectively, and then subjected to a series of tests using mass spectrometry to ascertain the suitability of the isotopic labels for peptide quantitation. The experiments show methyl iodide to be a very quantitative label, exhibiting a linear relationship in concentration over the dynamic range of the mass spectrometer used in the analysis (up to 4 orders of magnitude) both as pure samples and in a complex mixture of peptides. The tendency of trimethylated peptides to preferentially form a(2) fragment ions in MS(2) produces a significant increase in sensitivity, especially when the mass spectrometer is used in the MRM mode. Tests were also performed to verify the stability of the label against H/D exchange and its suitability for long-term storage, showing little degradation while in solution and during subsequent chemical processing.

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 score0.052
Threshold uncertainty score0.216

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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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

Citations12
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe AnalystSame topicAdvanced Proteomics Techniques and ApplicationsFrench-language works237,207