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Record W4407957436 · doi:10.1016/j.isci.2025.112105

M-Motif, a potential non-conventional NLS in YAP/TAZ and other cellular and viral proteins that inhibits classic protein import

2025· article· en· W4407957436 on OpenAlexafffund
Michael Kofler, Shruthi Venugopal, Caterina Di Ciano‐Oliveira, András Kapùs

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchKidney Foundation of Canada
KeywordsNLSMotif (music)Cell biologyChemistryComputational biologyBiologyBiochemistryNuclear localization sequencePhysicsGene

Abstract

fetched live from OpenAlex

Multiple mechanisms were proposed to mediate the nuclear import of TAZ/YAP, transcriptional co-activators regulating organ growth and regeneration. Our earlier observations showed that TAZ/YAP harbor a C-terminal, unconventional nuclear localization signal (NLS). Here, we show that this sequence, necessary and sufficient for basal, ATP-independent nuclear import, contains an indispensable central methionine flanked by negatively charged residues. Based on these features, we define the M-motif and propose that it is a new class of NLS, also present and import-competent in other cellular (STAT1 and cyclin B1) and viral (ORF6 of SARS-CoV2, VSV-M) proteins. Accordingly, ORF6 SARS-Cov2 competitively inhibits TAZ/YAP uptake, while TAZ abrogates STAT1 import. Similar to viral M-motif proteins, TAZ binds RAE1 and inhibits classic nuclear protein import, including that of antiviral factors (IRF3 and NF-κB). However, RAE1 is dispensable for TAZ import itself. Thus, the TAZ/YAP NLS has a dual function: it mediates unconventional nuclear import and inhibits classic import, contributing to the suppression of antiviral responses.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.526

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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