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Record W2749365053 · doi:10.1002/bab.1589

<i>In silico</i>mapping of allosteric ligand binding sites in type‐1 cannabinoid receptor

2017· article· en· W2749365053 on OpenAlexaff
Annalaura Sabatucci, Daniel Tortolani, Enrico Dainese, Mauro Maccarrone

Bibliographic record

VenueBiotechnology and Applied Biochemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsAllosteric regulationIn silicoBinding siteDocking (animal)ChemistryDrug discoveryComputational biologyLigand (biochemistry)StereochemistryHomology modelingAllosteric modulatorBinding pocketSmall moleculeReceptorBiochemistryBiologyEnzymeMedicine

Abstract

fetched live from OpenAlex

Abstract The recent resolution of the crystal structure of type‐1 cannabinoid receptor (CB1) and the discovery of novel modulators for this target open the way to the possibility of elucidating the structural requirements for CB1binding, and thereby facilitate a rational drug design. Compounds that target the orthosteric site of CB1in some cases have shown side effects. Allosteric modulators could potentially avoid these side effects by influencing binding and/or efficacy of orthosteric ligands. Here, we summarize and compare previous data on different putative allosteric binding sites observed in CB1homology models with anin silicodocking study of the recently published crystal structure of the same receptor on endogenous and natural hydrophobic ligands that act as positive allosteric modulators and negative allosteric modulators of CB1. In particular, a lipid‐exposed pocket targeted by most of the tested molecules is reported and discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.275
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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