Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 12;26(10):2857.
doi: 10.3390/molecules26102857.

An Integrated Approach toward NanoBRET Tracers for Analysis of GPCR Ligand Engagement

Michael P Killoran  1 , Sergiy Levin  2 , Michelle E Boursier  1 , Kristopher Zimmerman  1 , Robin Hurst  1 , Mary P Hall  1 , Thomas Machleidt  1 , Thomas A Kirkland  2 , Rachel Friedman Ohana  1
Affiliations

An Integrated Approach toward NanoBRET Tracers for Analysis of GPCR Ligand Engagement

Michael P Killoran et al. Molecules. .

Abstract

Gaining insight into the pharmacology of ligand engagement with G-protein coupled receptors (GPCRs) under biologically relevant conditions is vital to both drug discovery and basic research. NanoLuc-based bioluminescence resonance energy transfer (NanoBRET) monitoring competitive binding between fluorescent tracers and unmodified test compounds has emerged as a robust and sensitive method to quantify ligand engagement with specific GPCRs genetically fused to NanoLuc luciferase or the luminogenic HiBiT peptide. However, development of fluorescent tracers is often challenging and remains the principal bottleneck for this approach. One way to alleviate the burden of developing a specific tracer for each receptor is using promiscuous tracers, which is made possible by the intrinsic specificity of BRET. Here, we devised an integrated tracer discovery workflow that couples machine learning-guided in silico screening for scaffolds displaying promiscuous binding to GPCRs with a blend of synthetic strategies to rapidly generate multiple tracer candidates. Subsequently, these candidates were evaluated for binding in a NanoBRET ligand-engagement screen across a library of HiBiT-tagged GPCRs. Employing this workflow, we generated several promiscuous fluorescent tracers that can effectively engage multiple GPCRs, demonstrating the efficiency of this approach. We believe that this workflow has the potential to accelerate discovery of NanoBRET fluorescent tracers for GPCRs and other target classes.

Keywords: GPCRS; HiBiT; NanoBRET; in silico screen; ligand-engagement.

PubMed Disclaimer

Conflict of interest statement

All authors were Promega employees at time of manuscript preparation. The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Processing and analysis of GPCR-ligand interaction data. (A) Data processing scheme for preparing the training and test datasets for machine learning. Compounds from the GLASS database with known CHEMBL IDs were filtered for unique interactions with human GPCRs followed by removal of redundant structures and random selection of 530 unique interactions for each GPCR family. Compounds in the Selected dataset were encoded as molecular fingerprints and split into training and test datasets. (B) The number of compounds interacting with each GPCR family in the GLASS dataset before and after processing. GPCR family names are shown in Supplementary Materials Table S1. (C) UMAP projection of compounds in the Selected dataset highlights the spatial clustering of unique scaffolds used to train the machine learning model. For visualization, the UMAP algorithm embeds the multi-dimensional molecular fingerprints of the training set into a two-dimensional representation (UMAP 1 and UMAP 2) while preserving the essential topological structure of the data. Each compound is represented by a single point and colored according to its GPCR family interaction as in (B). Inset UMAP projections (right) show examples of the dense clustering observed for compounds interacting with specific GPCR families, highlighting the separation of family-specific ligands.
Figure 2
Figure 2
Metrics for machine learning model performance and example predictions. (A) GPCR families associated with the numbering scheme used in the figure. (B) Accuracy, sensitivity, and specificity metrics (%) of model predictions for compounds in the test dataset were calculated for each family and shown as a heatmap. Indicators of specific values in the legend for 30%, 60%, 90% (triangle, diamond, and star, respectively) are shown within each heatmap as a visual reference. (C) Confusion matrix comparing classification of compounds in the test dataset into their predicted versus true (annotated) GPCR target family. The value at each position in the heatmap represent the percentage of compounds in the test set classified into each GPCR family. (D) Example of model predictions for three unmodified molecular scaffolds. Model predictions are shown as a heatmap specifying the classification probability (%) for each GPCR family. Stars are used to mark predictions of known GPCR family interactions. Numerical values for all heatmaps are included as tables in Figures S3–S5.
Figure 3
Figure 3
Synthesis of modifiable Clozapine analogues. (A,B) Boc-protected “Clozapine-b” was prepared from commercially available Clozapine by a two-step procedure utilizing late-stage Ritter arene thianthrenation followed by Suzuki-Miyaura cross-coupling with potassium Boc-aminomethyltrifluoroborate. (C) Boc-protected “Clozapine-a” was synthesized from advanced intermediate C1 via reaction with 1-(3-N-Boc-propyl)-piperazine. (D) tBu-protected “Clozapine-c” was generated via copper-catalyzed carbenoid insertion of tBu-diazoacetate into aniline N-H bond of C1 followed by (E) imidoyl chloride substitution with N-methylpiperazine.
Figure 4
Figure 4
NanoBRET screen of Clozapine tracer candidates. (A) Illustration of the NanoBRET screening strategy for revealing the binding profile of fluorescent tracer candidates. Duplicates of cells transfected with 48 different HiBiT-GPCRs constructs per plate were treated with a fluorescent tracer either alone or in the presence of competing parental compound. Following complementation with purified LgBiT and measurement of BRET, specificity of interactions was evaluated through a decrease in BRET due to competitive displacement of the bound tracer by excess parental compound. Background BRET is shown in light blue, specific BRET is shown in red and a decrease in BRET due to competitive displacement is shown in lighter pink. (B) Binding profile of Clozapine tracer candidates. Modification sites are shown in Figure 3. NanoBRET screen across 184 HiBiT-GPCRs from 51 families revealed specific interactions (≥1.5-fold response) with GPCRs from five different families. Clozapine-a-2-NB590 had the broadest binding profile.
Figure 5
Figure 5
Saturation binding of Clozapine-a-2-NB590 to HiBiT-GPCRs from five families. (A) Structure of Clozapine-a-2-NB590. (B–F) Specific dose-dependent BRET measurements for HiBiT-GPCRs expressed in HEK293 cells. BRET values at each Clozapine-a-2-NB590 tracer concentration were background-corrected by subtracting parallel measurements taken in the presence of competing Clozapine. Data represent the mean ± S.D. of triplicates.
Figure 6
Figure 6
NanoBRET screen of Amitriptyline and AZD1283 tracer candidates. (A) Structure of Amitriptyline with modifiable positions marked. (B) NanoBRET screen across 184 HiBiT-GPCRs from 51 families revealed specific interactions (≥1.5-fold response) with GPCRs from seven families. Amitriptyline-b-2-NB590 had a substantially broader binding profile exhibiting specific interactions with 17 GPCRs. (C) Structure of AZD1283 with modifiable position marked. (D) NanoBRET screen revealed specific interactions (≥1.5-fold response) with 3 Purinergic GPCRs.
Figure 7
Figure 7
Machine learning predictions for GPCR-ligand interactions versus empirical NanoBRET screens. (A) Machine learning predictions for interactions between scaffolds A–Q (specified on the right) and 36 GPCR families. For each scaffold, predictions receiving a classification probability ≥4% were considered a positive interaction and are shown in red, with predicted negative interactions in white. (B) Empirical NanoBRET evaluations across the 36 GPCRs families using fluorescent tracers. For each scaffold, confirmed interactions with ≥1.5-fold response are shown in blue and negative or unconfirmed interactions are shown in white. (C) Concordance between machine learning predictions and NanoBRET screens. Confirmed predictions are shown in purple.
Figure 8
Figure 8
NanoBRET GPCR ligand-engagement assays facilitated by lead fluorescent tracers. A circular dendrogram showing GPCRs classified according to their family and class types [1]. NanoBRET ligand-engagement assays for the GPCRs highlighted in red were facilitated by lead fluorescent tracers developed in this study. Details on these assays including, the fluorescent tracers associated with each assay as well as tracers’ binding affinity and structures are included in Figures S12 and S13.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Hauser A.S., Attwood M.M., Rask-Andersen M., Schiöth H.B., Gloriam D.E. Trends in GPCR drug discovery: New agents, targets and indications. Nat. Rev. Drug Discov. 2017;16:829–842. doi: 10.1038/nrd.2017.178. - DOI - PMC - PubMed
    1. Wacker D., Stevens R.C., Roth B.L. How ligands illuminate GPCR molecular pharmacology. Cell. 2017;170:414–427. doi: 10.1016/j.cell.2017.07.009. - DOI - PMC - PubMed
    1. Stoddart L.A., White C.W., Nguyen K., Hill S.J., Pfleger K.D.G. Fluorescence- and bioluminescence-based approaches to study GPCR ligand binding. Br. J. Pharmacol. 2015;173:3028–3037. doi: 10.1111/bph.13316. - DOI - PMC - PubMed
    1. Sykes D.A., Stoddart L.A., Kilpatrick L.E., Hill S.J. Binding kinetics of ligands acting at GPCRs. Mol. Cell. Endocrinol. 2019;485:9–19. doi: 10.1016/j.mce.2019.01.018. - DOI - PMC - PubMed
    1. Boursier M.E., Levin S., Zimmerman K., Machleidt T., Hurst R., Butler B.L., Eggers C.T., Kirkland T.A., Wood K.V., Ohana R.F. The luminescent HiBiT peptide enables selective quantitation of G protein–coupled receptor ligand engagement and internalization in living cells. J. Biol. Chem. 2020;295:5124–5135. doi: 10.1074/jbc.RA119.011952. - DOI - PMC - PubMed

LinkOut - more resources