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A simple open source bioinformatic methodology for initial exploration of GPCR ligands' agonistic/antagonistic properties.

ABSTRACT: Drug development is an arduous procedure, necessitating testing the interaction of a large number of potential candidates with potential interacting (macro)molecules. Therefore, any method which could provide an initial screening of potential candidate drugs might be of interest for the acceleration of the procedure, by highlighting interesting compounds, prior to in vitro and in vivo validation. In this line, we present a method which may identify potential hits, with agonistic and/or antagonistic properties on GPCR receptors, integrating the knowledge on signaling events triggered by receptor activation (GPCRs binding to G_α,β,γ proteins, and activating G_α , exchanging GDP for GTP, leading to a decreased affinity of the G_α for the GPCR). We show that, by integrating GPCR-ligand and G_α -GDP or -GTP binding in docking simulation, which correctly predicts crystallographic data, we can discriminate agonists, partial agonists, and antagonists, through a linear function, based on the ΔG (Gibbs-free energy) of liganded-GPCR/G_α -GDP. We built our model using two G_αs (β2-adrenergic and prostaglandin-D₂ ), four G_αi (μ-opioid, dopamine-D3, adenosine-A1, rhodopsin), and one G_αo (serotonin) receptors and validated it with a series of ligands on a recently deorphanized G_αi receptor (OXER1). This approach could be a valuable tool for initial in silico validation and design of GPRC-interacting ligands.

SUBMITTER: Panagiotopoulos AA

PROVIDER: S-EPMC7358596 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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A simple open source bioinformatic methodology for initial exploration of GPCR ligands' agonistic/antagonistic properties.

Panagiotopoulos Athanasios A AA Papachristofi Christina C Kalyvianaki Konstantina K Malamos Panagiotis P Theodoropoulos Panayiotis A PA Notas George G Calogeropoulou Theodora T Castanas Elias E Kampa Marilena M

Pharmacology research & perspectives 20200801 4

Drug development is an arduous procedure, necessitating testing the interaction of a large number of potential candidates with potential interacting (macro)molecules. Therefore, any method which could provide an initial screening of potential candidate drugs might be of interest for the acceleration of the procedure, by highlighting interesting compounds, prior to in vitro and in vivo validation. In this line, we present a method which may identify potential hits, with agonistic and/or antagonis ...[more]

PMID: 32662237

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Project description:MotivationG 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.ResultsBioactivity 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 implementationpdCSM-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 informationSupplementary data are available at Bioinformatics Advances online.

Project description:BACKGROUND: The widespread use of genome sequencing provided evidences for the high degree of conservation in innate immunity signalling pathways across animal phyla. However, the functioning and evolutionary history of immune-related genes remains unknown for most invertebrate species. A striking observation coming from the analysis of the pea aphid Acyrthosiphon pisum genome is the absence of important conserved genes known to be involved in the antimicrobial responses of other insects. This reduction in antibacterial immune defences is thought to be related to their long-term association with beneficial symbiotic bacteria and to facilitate symbiont maintenance. An additional possibility to avoid elimination of mutualistic symbionts is a fine-tuning of the host immune response. To explore this hypothesis we investigated the existence and potential involvement of immune regulators in aphid agonistic and antagonistic interactions. RESULTS: In contrast to the limited antibacterial arsenal, we showed that the pea aphid Acyrthosiphon pisum expresses 5 members of Macrophage Migration Inhibitory Factors (ApMIF), known to be key regulators of the innate immune response. In silico searches for MIF members in insect genomes followed by phylogenetic reconstruction suggest that evolution of MIF genes in hemipteran species has been shaped both by differential losses and serial duplications, raising the question of the functional importance of these genes in aphid immune responses. Expression analyses of ApMIFs revealed reduced expression levels in the presence, or during the establishment of secondary symbionts. By contrast, ApMIFs expression levels significantly increased upon challenge with a parasitoid or a Gram-negative bacteria. This increased expression in the presence of a pathogen/parasitoid was reduced or missing, in the presence of facultative symbiotic bacteria. CONCLUSIONS: This work provides evidence that while aphid's antibacterial arsenal is reduced, other immune genes widely absent from insect genomes are present, diversified and differentially regulated during antagonistic or agonistic interactions.

Dataset Information

A simple open source bioinformatic methodology for initial exploration of GPCR ligands' agonistic/antagonistic properties.

Publications

A simple open source bioinformatic methodology for initial exploration of GPCR ligands' agonistic/antagonistic properties.

Similar Datasets

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