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Record W2067237567 · doi:10.1101/gr.6667007

Examining protein–protein interactions using endogenously tagged yeast arrays: The Cross-and-Capture system

2007· article· en· W2067237567 on OpenAlexafffund
Bernhard Suter, Michael J. Fetchko, Ralph Imhof, Christopher I. Graham, Ingrid Stoffel-Studer, Caroline Zbinden, Maanasa Raghavan, Lianet Lopez, Lucija Beneti, Jacqueline Hort, Jeffrey Fillingham, Jack Greenblatt, Guri Giaever, Corey Nislow, Igor Štagljar

Bibliographic record

VenueGenome Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteGenentechCanadian Institutes of Health ResearchGenome CanadaGebert Rüf StiftungOntario GenomicsOntario Genomics Institute
KeywordsBiologyYeastComputational biologyProtein–protein interactionProtein-fragment complementation assaySaccharomyces cerevisiaeFunction (biology)GeneticsDNATwo-hybrid screeningProtein functionGenePhenotype

Abstract

fetched live from OpenAlex

Comprehensive approaches to detect protein-protein interactions (PPIs) have been most successful in the yeast model system. Here we present "Cross-and-Capture," a novel assay for rapid, sensitive assessment of PPIs via pulldown of differently tagged yeast strain arrays. About 500 yeast genes that function in DNA replication, repair, and recombination and nuclear proteins of unknown function were chromosomally tagged with six histidine residues or triple VSV epitopes. We demonstrate that the assay can interrogate a wide range of previously known protein complexes with increased resolution and sensitivity. Furthermore, we use "Cross-and-Capture" to identify two novel protein complexes: Rtt101p-Mms1p and Sae2p-Mre11p. The Rtt101p-Mms1p interaction was subsequently characterized by genetic and functional analyses. Our studies establish the "Cross-and-Capture" assay as a novel, versatile tool that provides a valuable complement for the next generation of yeast proteomic studies.

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)0.003
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 score0.398
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.342
Teacher spread0.261 · 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

Citations28
Published2007
Admission routes2
Has abstractyes

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