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Record W2022095303 · doi:10.1016/j.febslet.2012.04.054

The language of SH2 domain interactions defines phosphotyrosine‐mediated signal transduction

2012· review· en· W2022095303 on OpenAlexaff
Bernard A. Liu, Brett W. Engelmann, Piers Nash

Bibliographic record

VenueFEBS Letters · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersNational Institute of General Medical SciencesNational Institutes of HealthUniversity of ChicagoNational Science Foundation
KeywordsSH2 domainComputational biologyPhosphotyrosine-binding domainProtein–protein interactionProto-oncogene tyrosine-protein kinase SrcSignal transductionBiologyComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

Natural languages arise in an unpremeditated fashion resulting in words and syntax as individual units of information content that combine in a manner that is both complex and contextual, yet intuitive to a native reader. In an analogous manner, protein interaction domains such as the Src Homology 2 (SH2) domain recognize and "read" the information contained within their cognate peptide ligands to determine highly selective protein-protein interactions that underpin much of cellular signal transduction. Herein, we discuss how contextual sequence information, which combines the use of permissive and non-permissive residues within a parent motif, is a defining feature of selective interactions across SH2 domains. Within a system that reads phosphotyrosine modifications this provides crucial information to distinguish preferred interactions. This review provides a structural and biochemical overview of SH2 domain binding to phosphotyrosine-containing peptide motifs and discusses how the diverse set of SH2 domains is able to differentiate phosphotyrosine ligands.

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: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.024
GPT teacher head0.310
Teacher spread0.286 · 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
GenreReview

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

Citations127
Published2012
Admission routes1
Has abstractyes

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