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Record W4416407035 · doi:10.1002/cbic.202500739

Development of a Genetically Encoded and Potent PDE6D Inhibitor

2025· article· en· W4416407035 on OpenAlexaff
Atanasio Gómez‐Mulas, Elisabeth Schaffner‐Reckinger, Hanne Peeters, Rohan Chippalkatti, Arnela Dautbasic, Matthew J. Smith, Shehab Ismail, Daniel Abankwa

Bibliographic record

VenueChemBioChem · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhosphodiesterase function and regulation
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersFonds Wetenschappelijk OnderzoekFonds National de la Recherche Luxembourg
KeywordsPrenylationSmall moleculeChemical biologyPlasma protein bindingHEK 293 cellsPhenotypic screeningMembrane proteinEffector

Abstract

fetched live from OpenAlex

PDE6D is a trafficking chaperone of prenylated proteins, such as small GTPases. Several small molecule inhibitors have been developed against it, given that the oncogene K-Ras is one of the cargo proteins. Inhibitor development suffers from the fact that inhibitors against the hydrophobic pocket of PDE6D are typically poorly water-soluble. Herein, the development of genetically encoded inhibitors that are inspired by high-affinity natural cargo of PDE6D is described. The most potent inhibitor, SNAP-STI, encodes merely a farnesylated tetra-peptide, which efficiently blocks PDE6D binding of farnesylated cargo. Direct comparison with small molecule PDE6D inhibitors suggests its higher potency. It is shown that inhibition of K-Ras membrane anchorage and K-RasG12C-dependent MAPK signaling by SNAP-STI is weak, consistent with what is observed after PDE6D knockdown. The data therefore further support that PDE6D is not a suitable surrogate target for efficient inhibition of K-Ras membrane anchorage and MAPK-activity. Nonetheless, by exploiting contacts at the pocket entry, a generalizable strategy to design high-affinity PDE6D inhibitors is established, providing powerful tools for PDE6D biology and target validation.

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 score0.022
Threshold uncertainty score0.314

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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