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Record W2410422668 · doi:10.1007/978-1-60327-394-7_7

Characterization of Kinase Target Phosphorylation Consensus Motifs Using Peptide SPOT Arrays

2009· review· en· W2410422668 on OpenAlexafffund
Genie Leung, James M. Murphy, Doug Briant, Frank Sicheri

Bibliographic record

VenueMethods in molecular biology · 2009
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsPhosphorylationKinaseProtein phosphorylationBiochemistryThreonineSerineCell biologyProteomeBiologyProtein kinase AChemistry

Abstract

fetched live from OpenAlex

The human proteome is known to contain >500 protein kinases, which regulate almost all facets of cellular biology by the post-translational attachment of a phosphate moiety to serine, threonine, or tyrosine residues within a substrate protein. Most protein kinases remain poorly characterized and, as a result, current studies are directed toward defining their target substrates experimentally to gain a comprehensive view of the signaling proteins and pathways modulated by these kinases. Herein, we describe a rapid and convenient method for elucidating the consensus substrate motif for phosphorylation by a protein kinase using peptide SPOT arrays that are custom-synthesized on a cellulose membrane support. The definition of the target consensus motif provides an important starting point for the identification of physiologically relevant kinase substrates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.091
GPT teacher head0.468
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations13
Published2009
Admission routes2
Has abstractyes

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