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Record W2592438379 · doi:10.1002/cpch.15

Detecting Membrane Protein‐protein Interactions Using the Mammalian Membrane Two‐hybrid (MaMTH) Assay

2017· article· en· W2592438379 on OpenAlexaff
Punit Saraon, Ingrid Grozavu, Sang Hyun Lim, Jamie Snider, Zhong Yao, Igor Štagljar

Bibliographic record

VenueCurrent Protocols in Chemical Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGreen fluorescent proteinProtein–protein interactionMembrane proteinContext (archaeology)Integral membrane proteinCell biologyBiologyMembraneTransfectionCloning (programming)Computational biologyCell cultureGeneBiochemistryGeneticsComputer science

Abstract

fetched live from OpenAlex

Protein-protein interactions (PPIs) play an integral role in numerous cellular processes. Membrane protein interactions, in particular, are critical in cellular responses to stresses and stimuli, with dysfunction of these PPIs (e.g., due to aberrant expression and/or mutation of interaction partners) leading to a diverse array of pathological states. Exploration of the interaction space and dynamics of membrane proteins is difficult due to the limitations of current techniques used to study proteins in the biochemically complex environment of biological membranes. In the protocols below, we describe a newly developed membrane protein interaction assay called the Mammalian-Membrane Two-Hybrid (MaMTH), designed specifically for the detection of integral membrane PPIs in the context of living mammalian cells. Prior to using MaMTH, cell lines of interest are genetically modified to encode a reporter of choice. MaMTH "bait" and "prey" constructs of interest are also generated using Gateway cloning technology. The assay is then performed by co-transfection of baits and preys, with bait-prey interaction quantifiably assessed by way of a reporter signal (e.g., light (luciferase), fluorescence (GFP). © 2017 by John Wiley & Sons, Inc.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.054
GPT teacher head0.385
Teacher spread0.331 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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