Hompluem, P. | Search bioRxiv

bioRxiv2024

Probing drug-target engagement of soluble Gαi1 protein using the SolThermoBRET thermal shift assay

IntroductionSensitive protein stability assays used during hit confirmation conventionally require high quantities of purified protein. Here, we describe a novel high-throughput 384-well BRET-based thermostability assay allowing for the ultrasensitive determination of Gi1 protein stability. Using this method we make use of the environmentally sensitive dye, SYPRO Red, which interacts with hydrophobic regions in the protein that become exposed upon denaturation. Assays are functional in crude cell lysates, without any requirement for protein purification. MethodsThe SolThermoBRET method measures resonance energy transfer between a thermally stabilised nano-luciferase (tsNLuc) genetically engineered to the N-terminus of Gi1 protein, and SYPRO Red, a fluorescent dye that binds to lipophilic residues exposed upon protein unfolding in response to thermal denaturation. Samples in 384-well PCR plate were subjected to a temperature gradient (30 - 60 {degrees}C) on a PCR block and incubated for 5 minutes. Isothermal SolThermoBRET at a fixed temperature 45{degrees}C allowed the measurement of IC50 curves to various ligands. Furimazine (10 M) was added to the samples and BRET read on the BMG Labtech PHERAstar FSX at room temperature. Melt curves were fit to a Boltzmann sigmoidal equation to obtain Tm values. ResultsLigand-induced stabilisation of Gi1 protein was demonstrated in non-purified samples. A range of Tm values were measured with increasing stabilisation observed for nucleotides/sides in the following order GDP<GTP<GppNHp<GTP{gamma}S. Correlation was observed between SolThermoBRET derived Tms determined in the presence of high ligand concentrations and IC50 determinations using Isothermal SolThermoBRET. ConclusionsSolThermoBRET represents a sensitive nanoscale method for the detection of changes in soluble protein thermostability. This method is attractive for hit conformation and the identification of novel ligands targeting soluble proteins.

biophysics↗

bioRxiv2021

Prediction of ligand-receptor pharmacological activities using a combined docking and machine learning approach

G protein coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands which bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesised that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over-represented. We computationally docked ~2700 known {beta}2AR ligands to multiple {beta}2AR structures, generating ca 75,000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure-activity relationship that identifies small changes to the ligands that invert their activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.

bioinformatics↗