Show simple item record

dc.contributor.authorVelloso, João Paulo L
dc.contributor.authorAscher, David B
dc.contributor.authorPires, Douglas EV
dc.descriptionFunder: Newton Fund RCUK-CONFAP
dc.descriptionFunder: Victorian Government’s Operational Infrastructure Support Program
dc.description.abstractMOTIVATION: G protein-coupled receptors (GPCRs) can selectively bind to many types of ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and neurotransmitters, modulating cell physiology. Considering their role in many essential cellular processes, they are one of the most targeted protein families, with over a third of all approved drugs modulating GPCR signalling. Despite this, the large diversity of receptors and their multipass transmembrane architectures make the identification and development of novel specific, and safe GPCR ligands a challenge. While computational approaches have the potential to assist GPCR drug development, they have presented limited performance and generalization capabilities. Here, we explored the use of graph-based signatures to develop pdCSM-GPCR, a method capable of rapidly and accurately screening potential GPCR ligands. RESULTS: Bioactivity data (IC50, EC50, Ki and Kd) for individual GPCRs were curated. After curation, we used the data for developing predictive models for 36 major GPCR targets, across 4 classes (A, B, C and F). Our models compose the most comprehensive computational resource for GPCR bioactivity prediction to date. Across stratified 10-fold cross-validation and blind tests, our approach achieved Pearson's correlations of up to 0.89, significantly outperforming previous methods. Interpreting our results, we identified common important features of potent GPCRs ligands, which tend to have bicyclic rings, leading to higher levels of aromaticity. We believe pdCSM-GPCR will be an invaluable tool to assist screening efforts, enriching compound libraries and ranking candidates for further experimental validation. AVAILABILITY AND IMPLEMENTATION: pdCSM-GPCR predictive models and datasets used have been made available via a freely accessible and easy-to-use web server at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
dc.publisherOxford University Press (OUP)
dc.rightsAttribution 4.0 International
dc.sourcenlmid: 9918282081306676
dc.sourceessn: 2635-0041
dc.titlepdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures.
prism.publicationNameBioinform Adv
dc.contributor.orcidAscher, David B [0000-0003-2948-2413]
dc.contributor.orcidPires, Douglas EV [0000-0002-3004-2119]
pubs.funder-project-idInvestigator Grant from the National Health and Medical Research Council (NHMRC) of Australia (GNT1174405)
pubs.funder-project-idCoordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (001)
pubs.funder-project-idThe Medical Research Council (MR/M026302/1)
pubs.funder-project-idWellcome Trust (093167/Z/10/Z)

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International