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Bioinform Adv

Title:		pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures
Author(s):		Velloso JPL; Ascher DB; Pires DEV;
Address:		"Fundacao Oswaldo Cruz, Instituto Rene Rachou, Belo Horizonte 30190-009, Brazil. Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne 3052, Australia. Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne 3052, Australia. Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Australia. Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil. Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne 3052, Australia. Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK. School of Computing and Information Systems, University of Melbourne, Melbourne 3053, Australia"
Journal Title:		Bioinform Adv
Year:		2021
Volume:		20211110
Issue:		1
Page Number:		vbab031 -
DOI:		10.1093/bioadv/vbab031
ISSN/ISBN:		2635-0041 (Electronic) 2635-0041 (Linking)
Abstract:		"MOTIVATION: 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 http://biosig.unimelb.edu.au/pdcsm_gpcr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online"
Keywords:
Notes:		"PubMed-not-MEDLINEVelloso, Joao Paulo L Ascher, David B Pires, Douglas E V eng WT_/Wellcome Trust/United Kingdom MR/M026302/1/MRC_/Medical Research Council/United Kingdom England 2021/12/14 Bioinform Adv. 2021 Nov 10; 1(1):vbab031. doi: 10.1093/bioadv/vbab031. eCollection 2021"