Surface Plasmon Resonance as a Tool for Ligand Binding Investigation of Engineered GPR17 Receptor, a G Protein Coupled Receptor Involved in Myelination

Abstract

The aim of this study was to investigate the potential of surface plasmon resonance (SPR) spectroscopy for the measurement of real-time ligand-binding affinities and kinetic parameters for GPR17, a G protein-coupled receptor (GPCR) of major interest in medicinal chemistry as potential target in demyelinating diseases. The receptor was directly captured, in a single-step, from solubilized membrane extracts on the sensor chip through a covalently bound anti-6x-His-antibody and retained its ligand binding activity for over 24 h. Furthermore, our experimental setup made possible, after a mild regeneration step, to remove the bound receptor without damaging the antibody, and thus to reuse many times the same chip. Two engineered variants of GPR17, designed for crystallographic studies, were expressed in insect cells, extracted from crude membranes and analyzed for their binding with two high affinity ligands: the antagonist Cangrelor and the agonist Asinex 1. The calculated kinetic parameters and binding constants of ligands were in good agreement with those reported from activity assays and highlighted a possible functional role of the N-terminal residues of the receptor in ligand recognition and binding. Validation of SPR results was obtained by docking and molecular dynamics of GPR17-ligands interactions and by functional in vitro studies. The latter allowed us to confirm that Asinex 1 behaves as GPR17 receptor agonist, inhibits forskolin-stimulated adenylyl cyclase pathway and promotes oligodendrocyte precursor cell maturation and myelinating ability.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

“…http://www.mdckpred.in/ Regression model An intuitive way of prioritizing small molecules based on calculated MDCK permeabilities [ 404 ] Vienna LiverTox Prediction of interactions profiles of small molecules with transporters relevant for regulatory agencies. https://livertox.univie.ac.at/ Machine learning classification model Identify pharmacokinetic properties [ 405 ] Ambit-SMIRKS A software module for reaction representation, reaction search and structure transformation. http://ambit.sourceforge.net/smirks The Chemistry Development Kit Standardization of large chemical databases and pathway transformation database and prediction [ 406 ] COSMOfrag A Novel Tool for High-Throughput ADME Property Prediction and Similarity Screening Quantum Chemistry In the COSMO − RS picture, any molecular information is gathered in the so-called σ profiles, COSMOfrag replaces the single σ profile with a composition of partial σ profiles, selected by the use of extensive similarity searching algorithms [ 407 ] RosENet Predicting the absolute binding affinity of protein–ligand complexes Convolutional neural networks Combines voxelized molecular mechanics energies and molecular descriptors [ 408 ] MDeePred Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery.…”

Oligodendrocyte Dysfunction in Amyotrophic Lateral Sclerosis: Mechanisms and Therapeutic Perspectives

“…In both cases, the use of GPR17 agonist resulted in a significant amelioration of OL differentiation and myelination capabilities. Indeed, the selective GPR17 agonist Asinex 1 promoted OPC maturation and myelination of dorsal root ganglion (DRG) neurons in vitro [ 202 ]. Of note, a recent in vivo study showed that the treatment with another selective agonist, named galinex, resulted in protection against EAE symptoms exacerbation [ 203 ].…”

Development of the first in vivo GPR17 ligand through an iterative drug discovery pipeline: A novel disease-modifying strategy for multiple sclerosis

“…ligands [29,31,44]. The docking solutions were ranked according to their predicted binding free energy values.…”

“…However, these results are not surprising since all compounds of Family I derive from a common scaffold with agonist activity. Of note, previous independent virtual screening on the receptor led to the identification of GPR17 agonists with demonstrated promyelinating activity on in vitro rodent models characterized by similar structure [23,31,44] and potency, suggesting that this scaffold may be suitable for defining a lead compound for GPR17. Moreover, this potency is in line with that of other class-A GPCR agonists [56], and of our previous set of novel GPR17 activators [31].…”

“…The preventive administration of compound 9 significantly delayed the symptomatic onset of EAE, suggesting the predictivity of our in silico and in vitro pipeline, which has therefore the potential to be successfully used to identify other in vivo protective compounds. Based not only on specific in silico strategy, but also on the selectivity study carried out on the 40 most similar and more phylogenetically closely related class-A GPCRs [44], we believe the beneficial effects PLOS ONE of 9 be due to a specific protective interaction with GPR17 on resident oligodendrocytes, the only cells expressing the receptor in this model [15,20]. However, since one of the main challenges for new chemical entities are the off-target effects, future studies should be also focussed on the investigation of other possible non-specific targets of this compound.…”