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Record W4289668368 · doi:10.1039/d2cb00106c

QSAR models reveal new EPAC-selective allosteric modulators

2022· article· en· W4289668368 on OpenAlexafffund
Hebatallah Mohamed, Hongzhao Shao, Madoka Akimoto, Patrick Darveau, Marc R. Mackinnon, Jakob Magolan, Giuseppe Melacini

Bibliographic record

VenueRSC Chemical Biology · 2022
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsQuantitative structure–activity relationshipGuanine nucleotide exchange factorAllosteric regulationChemistryComputational biologyPartial agonistIntrinsic activityMode of actionGTPaseCombinatorial chemistryAgonistStereochemistryBiochemistryReceptorBiology

Abstract

fetched live from OpenAlex

Exchange proteins directly activated by cAMP (EPAC) are guanine nucleotide exchange factors for the small GTPases, Rap1 and Rap2. They regulate several physiological functions and mitigation of their activity has been suggested as a possible treatment for multiple diseases such as cardiomyopathy, diabetes, chronic pain, and cancer. Several EPAC-specific modulators have been developed, however studies that quantify their structure-activity relationships are still lacking. Here we propose a quantitative structure-activity relationship (QSAR) model for a series of EPAC-specific compounds. The model demonstrated high reproducibility and predictivity and the predictive ability of the model was tested against a series of compounds that were unknown to the model. The compound with the highest predicted affinity was validated experimentally through fluorescence-based competition assays and NMR experiments revealed its mode of binding and mechanism of action as a partial agonist. The proposed QSAR model can, therefore, serve as an effective screening tool to identify promising EPAC-selective drug leads with enhanced potency.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.029
GPT teacher head0.289
Teacher spread0.260 · 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 designTheoretical or conceptual
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

Citations6
Published2022
Admission routes2
Has abstractyes

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