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Record W2038601075 · doi:10.1126/science.1174301

Positive Selection of Tyrosine Loss in Metazoan Evolution

2009· article· en· W2038601075 on OpenAlexaff
Chris Soon Heng Tan, Adrian Pasculescu, Wendell A. Lim, Tony Pawson, Gary D. Bader, Rune Linding

Bibliographic record

VenueScience · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersNational Institute of General Medical Sciences
KeywordsMulticellular organismBiologyEvolutionary biologyReceptor tyrosine kinaseSelection (genetic algorithm)TyrosineLineage (genetic)PhenotypeOrganismComputational biologyGeneGeneticsComputer scienceSignal transductionArtificial intelligence

Abstract

fetched live from OpenAlex

Cataloging Kinase Targets Protein phosphorylation is a central mechanism in the control of many biological processes (see the Perspective by Collins ). It remains a challenge to determine the complete range of substrates and phosphorylation sites altered by a kinase like cyclin-dependent kinase 1 (Cdk1), which controls cell division in yeast. Holt et al. (p. 1682 ) engineered a strain of yeast to express a modified Cdk1 molecule that could be inhibited by a specific small-molecule inhibitor. The range of Cdk1-dependent phosphorylation was assessed by quantitative mass spectrometry, which revealed many previously uncharacterized substrates for Cdk1. In addition to phosphorylation on serine and threonine residues, which appears to be evolutionarily ancient, tyrosine phosphorylation occurs primarily in multicellular organisms. Tan et al. (p. 1686 , published online 9 July) compared the overall presence of tyrosine residues in human proteins (which are frequently phosphorylated) and in yeast proteins (which are not). Loss of tyrosine residues has occurred during evolution, presumably to reduce adventitious tyrosine phosphorylation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.168

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 head0.004
GPT teacher head0.252
Teacher spread0.248 · 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

Citations95
Published2009
Admission routes1
Has abstractyes

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