An integrated approach toward nanobret tracers for analysis of gpcr ligand engagement

Article abstract of DOI:10.3390/molecules26102857

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.

Full text of DOI:10.3390/molecules26102857

molecules
Article
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 and Rachel Friedman Ohana 1,
*
1
Promega Corporation, 2800 Woods Hollow, Fitchburg, WI 53711, USA; mike.killoran@promega.com (M.P.K.);
michelle.boursier@gmail.com (M.E.B.); Kris.Zimmerman@promega.com (K.Z.);
robin.hurst@promega.com (R.H.); Mary.Hall@promega.com (M.P.H.);
Thomas.Machleidt@promega.com (T.M.)
Promega Biosciences LLC, 277 Granada Drive, San Luis Obispo, CA 93401, USA;
serge.levin@gmail.com (S.L.); thomas.kirkland@promega.com (T.A.K.)
Correspondence: rachel.ohana@promega.com; Tel.: +1-608-274-1181
2
*
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.
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀꢁꢂꢃꢄꢅꢆ
Citation: Killoran, M.P.; Levin, S.;
Boursier, M.E.; Zimmerman, K.;
Hurst, R.; Hall, M.P.; Machleidt, T.;
Kirkland, T.A.; Friedman Ohana, R.
An Integrated Approach toward
NanoBRET Tracers for Analysis of
GPCR Ligand Engagement. Molecules
2021, 26, 2857. https://doi.org/
10.3390/molecules26102857
Academic Editor: Ingebrigt Sylte
Keywords: GPCRS; ligand-engagement; NanoBRET; HiBiT; in silico screen
Received: 15 April 2021
Accepted: 7 May 2021
Published: 12 May 2021
1. Introduction
G protein-coupled receptors (GPCRs) are among the most widely studied pharma-
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
cological targets [
1
,2]. They are substantially involved in regulation of multiple physio-
logical processes and their malfunction has been implicated in numerous diseases [1,2
].
Consequentially, detailed understanding of their engagement with bioactive compounds,
including binding affinity and kinetics, is imperative to both drug discovery and basic
research. The realization that the cellular environment can influence these measurements
has prompted the development of cell-based ligand-engagement assays as an alternative to
traditional biochemical radioligand binding. These assays exploit the inherent distance con-
straints of resonance energy transfer to detect molecular proximity between GPCRs labeled
with an energy donor (i.e., fluorescent protein, chromophore, or luciferase) and their lig-
Copyright:
© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
ands labeled with an energy acceptor fluorophore (fluorescent tracers) [
assays, NanoLuc luciferase-based bioluminescence resonance energy transfer (NanoBRET)
has been reported to be sensitive, robust, and capable of real-time measurements [ ].
3,4]. Among these
5–8
The bright luminescence generated by NanoLuc-based energy donors (i.e., full length
NanoLuc or HiBiT complemented with LgBiT, a complementation system derived from
Molecules 2021, 26, 2857. https://doi.org/10.3390/molecules26102857
https://www.mdpi.com/journal/molecules

Molecules 2021, 26, 2857
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NanoLuc) [
9,
10] enabled GPCRs ligand-engagement measurements previously impossible
with other luciferases [
has been especially useful for GPCRs studies [
5
–
7
,
11]. The HiBiT/LgBiT high affinity complementation reporter
12]. This is predominantly due to the cell
5
,
impermeability of LgBiT, which limits signal from complementation with HiBiT-tagged
GPCRs to the cell surface, eliminating intracellular background luminescence and resulting
in assays with overall greater sensitivity, dynamic range, and robustness [5].
Resonance energy transfer ligand-engagement assays offer specific and sensitive mea-
surements of molecular proximity between fluorescent tracers and their cognate GPCRs
genetically fused to an energy donor. Typically, engagement characteristics for unmodi-
fied ligands are revealed through competitive displacement of a bound fluorescent tracer.
However, rapid and efficient generation of receptor specific tracers is often challenging.
Conjugation of a fluorophore to a bioactive compound introduces a significant modification
to the chemical structure, frequently disrupting critical binding interactions and altering
physiochemical properties [13,14]. Therefore, judicious choice of fluorophore, linker chem-
istry, and conjugation site are all important for preserving the relevant properties of a
parental compound. Optimizing each of these aspects can be a labor-intensive process.
The exquisite specificity of resonance energy transfer ligand-engagement assays gov-
erned by binding to targets tethered to an energy donor offers an opportunity to utilize
promiscuous fluorescent tracers [15,16]. Such tracers can enable ligand-engagement assays
for multiple targets, thereby reducing the requirement of developing a selective tracer
for each target. However, unlike other target classes such as kinases [16], GPCR families
share little structural homology in their ligand binding pockets or cross reactivity among
their natural ligands [1,2,17]. Therefore, identification of compounds with a promiscuous
GPCR binding profile that are suitable for development into fluorescent tracers poses a
significant challenge. Taken together, there is a need for a creative approach to accelerate
the identification, modification, and evaluation of fluorescent tracer candidates for GPCR
ligand-engagement assays.
Ligand-based in silico screening can help to address part of this challenge by pro-
viding a means to rapidly search a vast chemical space for bioactive compounds with a
promiscuous binding profile [17
of known ligands, it can identify patterns associated with receptor interactions in an au-
tomated fashion without any knowledge of a receptor’s 3D structure [17 19]. Machine
–20]. When designed to exploit the chemical information
,
learning algorithms, are particularly well suited for pattern recognition and generalization
tasks. When trained to identify a shared set of chemical features among compounds known
to bind a specific target or target family, they can use those chemical signatures to identify
other compounds likely to interact with the targets. This approach can guide an automated,
preliminary selection of promiscuous compounds for highly diverse and selective target
classes like GPCRs, for which manual searches would be very challenging.
Compound modification with a fluorophore provides an additional opportunity to
improve the efficiency of tracer development. As mentioned above, the attachment of
a fluorophore to a bioactive compound requires careful selection of a conjugation site.
Often, the optimal site cannot be rationalized via structure activity relationships (SAR)
and/or in silico docking analysis, requiring empirical evaluation of several conjugation
sites. In addition, synthetic accessibility to such sites through de novo total synthesis
is often complex and can be labor and time intensive. Recently, late-stage modification
of organic compounds has emerged as a powerful approach for rapid exploration of
chemical space [21–23]. Compared to de novo synthesis, this approach often results in
significant time and cost savings by allowing modification of commercially available
compounds. However, the structural complexity of many bioactive compounds remains
a significant challenge for a site selective late-stage modification. Still, the toolbox of
late-stage functionalization chemistries continues to expand, providing additional tools for
rapid modification of an ever-growing list of chemical moieties.
Here, we describe an integrated workflow utilizing the approaches outlined above to
accelerate the generation and evaluation of fluorescent tracers for GPCRs. To demonstrate

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the efficiency of this workflow, we used a machine learning model to predict the binding
profiles of 17 model scaffolds and a blend of synthetic strategies to conjugate these scaffolds
to a fluorophore. The resulting panel of fluorescent tracer candidates was evaluated in a
NanoBRET ligand-engagement screen across a library of 184 HiBiT-tagged GPCRs. The
screen revealed a good overall correlation with model predictions and provided valuable
feedback on tracers’ binding profile, suitable conjugation site, and ultimately potential to
deliver robust ligand-engagement assays. Thus far, this strategy has resulted in several
fluorescent tracers that can effectively engage multiple receptors from one or more GPCR
families. We believe this workflow has the potential to accelerate tracer discovery and to
broaden the target classes amenable for NanoBRET ligand-engagement analyses.
2. Results and Discussion
2.1. Design of a Machine Learning Model for Predicting Interactions between Ligands and Their
GPCR Target Families
The high specificity afforded by NanoBRET allows utilization of promiscuous fluo-
rescent tracers for selective measurements at specific HiBiT-GPCRs, reducing the need to
develop a specific tracer for each target. Given the abundance of available information on
GPCR-ligand interactions in public databases, we sought to design an in silico screening
strategy to identify compounds exhibiting a propensity for promiscuous binding inter-
actions. We were motivated to use only chemical structures of known GPCR ligands as
input data for machine learning since this would provide access to the most comprehensive
compound set available, including those targeting GPCRs lacking 3-D structural informa-
tion. The resulting model could be used to screen compounds with a minimal amount of
chemical information needed.
The intent of our model design was to recognize common structural patterns shared
by ligands of specific GPCR families and then use that information to predict the target
family interactions for other compounds in an automated fashion. We chose to catego-
rize compounds based on their target GPCR families rather than individual receptors
for two reasons. First, the primary application of the model is to provide guidance for
identifying compounds that could interact promiscuously across a broad range of GPCRs
rather than with specific receptors or GPCR families. Second, it simplifies the modeling
task by providing greater differentiation between classes, under the assumption that lig-
ands interacting with receptors from different families are more likely to exhibit larger
differences, which in turn are easier to distinguish by the model. In practice, this approach
has made in silico screening of large datasets more tractable. Promiscuity across differ-
ent GPCR families is expected to be a rare property and by grouping compounds into
larger GPCR-family target classes it was easier to identify those with a high probability for
multi-target interactions.
Throughout model development, we identified several steps critical to the success of
this strategy including aspects of data processing as well as methods of model training and
validation. Briefly, we processed our chemical structure dataset by focusing on minimizing
sources of redundancy and bias while maximizing the diversity of chemical information
available for machine learning. Using chemical structures encoded as molecular finger-
prints we trained the model to recognize patterns of shared molecular features among
compounds that bind the same target class (GPCR family). The capacity of the model
to use those signature patterns to classify compounds into their GPCR target families
was validated using a test set comprising 30% of the compounds, which were withheld
during training.
2.2. Data Acquisition and Preparation for Machine Learning
Training a machine learning model to recognize patterns of molecular interactions
with different GPCR families requires a large, representative dataset comprising similarly
diverse ligands for each family. Among available data sources, we chose the GPCR
ligand-associated (GLASS) database as it provided an easily accessible and comprehensive

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dataset of known GPCR ligands [24]. However, since this approach requires only chemical
structures and their known GPCR target families as input, other data sources such as
results from high-throughput screenings or alternative databases could be used instead or
in supplement to the information used in this study.
Data acquired from the GLASS database contained over a million ligand-receptor
interactions for GPCRs from multiple species. Since we were primarily focused on develop-
ing NanoBRET tracers to interrogate ligand engagement of human GPCRs, we first filtered
out all redundant molecular structures and subsequently selected for ligands known to
target human receptors (Figure 1A). Comparison of the GLASS datasets before and after
processing indicated that 45 GPCR families each retained over 500 unique compounds
(Figure 1B). Ideally, the subsets used to train and evaluate the machine learning model
would uniformly represent the diversity of ligands associated with each GPCR target fam-
ily. Accordingly, to eliminate family imbalances and retain most of the starting chemical
diversity, we assembled a “Selected dataset” comprising an equal number of 530 random
compounds targeting each of the remaining GPCR families. We used 70% of these com-
pounds (i.e., 371 compounds per family) as a training set while the remaining 30%, which
were withheld during training, were used as a test set. During the development of the
model, we explored a range of training set sizes and found that these selection criteria
provided the best predictive classification for compounds in our test set (Supplementary
Materials Figure S1).
The 23,850 compounds in our Selected dataset were converted into a vector format,
which can serve as input for machine learning by encoding chemical structures as molecular
fingerprints. Chemical information encoded as molecular fingerprints has been successfully
used in machine learning tasks [25] and can include many formats ranging from the pres-
ence of substructures to descriptors of topological states or electronic environments [26–28]
.
Throughout the development of our final machine learning model, we systematically eval-
uated over a dozen different models utilizing single or multiple molecular fingerprinting
schemes for their capacity to classify compounds in our test set (Supplementary Materials
Figure S2). For each compound, the GPCR family with the highest classification probability
was compared to the known target family as annotated in the GLASS database. We found
that models based on fingerprint schemes exhibiting high overall accuracy (i.e., the true
positive and negative predictions) but having low correlation among their true positive
predictions performed best in combination, synergistically improving classification accu-
racy for compounds in our test set. However, improvements in test set accuracy did not
always translate to higher correlation between model predictions and empirical NanoBRET
screening data across all the tested GPCR families. In the end, our focus on identifying
scaffolds exhibiting promiscuous binding properties led us to select the model with the
highest accuracy on empirical NanoBRET data rather than on the test set. Overall, we
found that the effectiveness of training the machine learning model was highly dependent
on the structure of the training dataset, choice of molecular fingerprint representations,
and the classification task the model is applied to.
2.3. Analysis of the Machine Learning Dataset Using UMAP
Given the dependence of our model’s performance on the structure of the training set,
we sought to further investigate the structural relationships among compounds used for
machine learning. Using the chemical information encoded by molecular fingerprints, we
analyzed the underlying structure of the Selected dataset via Uniform Manifold Approxi-
mation and Projection (UMAP), an unsupervised learning algorithm [29]. UMAP reduces
the high dimensional chemical information of fingerprints into lower dimensions while
preserving the global structure of the dataset, enabling visualization and identification of
latent patterns. Representation of the Selected dataset in this manner, which can be used
as a measure of global chemical similarity, resulted in several large, independent clusters
(Figure 1C). In addition, many small but dense single-color areas, which represent highly
similar compounds that target specific GPCR families were noticeable. This latter type of

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pattern would be expected in data from large repositories that aggregate results for many
target-specific scaffolds and closely related analogs.
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. (
the GLASS dataset before and after processing. GPCR family names are shown in Supplementary
Materials Table S1. ( ) UMAP projection of compounds in the Selected dataset highlights the spatial
B) The number of compounds interacting with each GPCR family in
C
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.
Comparing the clustering patterns in UMAP space for individual GPCR families with
their model’s performance metrics provided additional insights into the influence of data

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structure on model fitting. For example, compounds associated with GPCR families for
which the model delivered classifications with high accuracy and sensitivity, such as the
Melatonin receptor family, also displayed tight clustering in UMAP space (Figure 1C, top
right). This suggested these compounds share a greater number of common structural
features, which could be more easily associated with their target family during model
training. In contrast, compounds associated with GPCR families for which the model
delivered classifications with lower sensitivity such as 5-Hydroxytryptamine receptors,
displayed a larger spread in UMAP space. This likely reflects fewer common patterns
among the chemical features of compounds associated with these families, which makes
fitting with a model more difficult (Figure 1C, bottom right).
One of the limitations using data acquired from large databases in this way is the
difficulty of confirming information aggregated from multiple sources. Accordingly, several
explanations are possible for the lower accuracy and misclassification of the model for
some individual GPCR families, including low affinity interactions or assay-specific results.
Still, the observed family-specific clustering in UMAP space for the majority of compounds
indicates that our training set encoded sufficient chemical information to separate these
high dimensional patterns when classifying compounds by their target GPCR family.
2.4. Development of a Machine Learning Model Classifying GPCR-Ligand Interactions
To provide a functional association between ligands and their known GPCR target
families, we trained a random forest model to recognize relevant patterns of molecular
fingerprints in our training set and to classify them by their annotated GPCR target families
in the GLASS database. The simultaneous use of many uncorrelated decision trees by
random forest algorithms provides several advantages for modeling the type of data in
our training set. For example, they perform well on large datasets with multiple features,
are robust to outliers and redundancy, and excel at error minimization during multi-class
predictions tasks [30]. After training the model, we used our test set to independently
evaluate its performance. For the 7155 compounds across 45 different GPCR families
we observed a total classification accuracy of 80.9%. Overall, this result indicated that
the machine learning model was able to successfully fit a function relating molecular
fingerprints to specific GPCR target families and to make accurate predictions on a set of
previously unseen compounds. As a control, we trained a model with randomized labels
of ligand-GPCR family associations. We found that classification accuracy for the test set
dropped significantly, down to 0.02%, the expected value for random classification across
the 45 GPCR families (Supplementary Materials Figure S1). This result confirmed the
model was specific in associating molecular fingerprint patterns with GPCR target families.
2.5. Evaluation of Model Predictions for Individual GPCR Families
To understand our model’s performance in more detail, we calculated accuracy, sensi-
tivity, and specificity metrics for each GPCR family (Figure 2B and Figure S3). Accuracy on
a per-family basis was calculated as the percentage of compounds correctly classified to
their target family, using their GLASS database annotation as the ground truth. Sensitivity
and specificity were calculated as the rate of positive and negative interactions that were
correctly classified, respectively. We note that given the large number of true negative
interactions being predicted across GPCR families, the specificity metric was potentially
biased toward high values.

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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.
We found that the model had 60% or better classification accuracy across all GPCR
families in our test set with 30/45 families exhibiting greater than 90% accuracy, indicating
a high level of performance for the majority of compounds in our test set. Sensitivity
for the same 30 GPCR families was 80% or better. Notably, the sensitivity values for
Glucagon and Parathyroid receptors were below 50%, which is likely due to the high rate
of misclassification among compounds targeting these two families, as discussed below.
Finally, all GPCR families in our test set exhibited specificity values greater than 98%,
consistent with the high values that might be expected for multiclass classification tasks
with predominantly negative predictions.
Analysis of test set predictions as a confusion matrix provided additional insights
into model performance for individual GPCR target families (Figure 2C and Figure S4).

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For most families, model accuracy was high with small errors dispersed among classes.
In the few cases with higher levels of misclassification, errors could often be attributed
to compounds targeting closely related families. For example, the model frequently mis-
classified compounds targeting the Vasopressin and Oxytocin families, which interact
with similar peptide ligands and share functions in neural cognition and behavior [31
]
(Figure S4). Similarly, the model misclassified compounds targeting the Glucagon and
Parathyroid families, which are also peptide-binding receptors that share a high level of
similarity in their structural mechanism for ligand binding [32]. Therefore, the low sensi-
tivity for these families could be explained by the natural promiscuity of their ligands. In
addition, compounds targeting these families displayed a diffuse spatial pattern in UMAP
space, indicating a high degree of heterogeneity in their molecular fingerprints, which
is challenging for identification of family-specific patterns. While our model delivered
good classification accuracy across many peptide and lipid binding GPCRs, we cannot
exclude the possibility that our use of molecular fingerprint designed for small molecules
may have contributed to lower sensitivity for some receptors that naturally bind peptides,
polypeptides, or lipids. Further refinement of the machine learning strategy to accom-
modate the unique properties of ligands targeting these specific GPCR families would
be expected to improve the sensitivity of the model. This could be achieved by training
models with molecular fingerprints representations optimized for specific ligand types,
although exploring this strategy was outside the scope of this study.
2.6. Extending Model Predictions to Identify Promiscuous GPCR Ligands
Having observed good classification accuracy on our test set, we extended the use
of our model’s predictions to identify compound promiscuity across GPCR families. Up
to now, for each compound, the GPCR family with the highest classification probability
was chosen as the predicted target class. We hypothesized that the generalizable principles
associating molecular fingerprint patterns with specific GPCR families might be reflected
in the classification probability distribution across families, thereby providing insight
into a compound’s propensity for promiscuous binding. Accordingly, to screen for multi-
target binders we used an empirically determined minimum threshold to include lower
probability predictions as potential interactions. Repurposing the classification probabilities
output of our random forest model in this way enabled us to extract additional information
and predict compound binding to all 45 GPCR target families in our dataset.
To evaluate use of the model for this purpose, we tested its ability to predict interac-
tions for three known GPCR ligands chosen on the basis of their varying selectivity for
different GPCR families: Clozapine [33], Xanthine amine congener (XAC) [34], and Nal-
trexone [35]. Clozapine, an atypical antipsychotic drug, represents a known promiscuous
ligand that interacts with receptors in the Acetylcholine, alpha-Adrenergic, Histamine,
Dopamine, and 5-Hydroxytryptamine families. In contrast, XAC and the addiction treat-
ment drug Naltrexone are regarded as family-selective ligands of Adenosine and Opioid
receptors, respectively. For all three ligands, the model’s classification probabilities output
successfully predicted their known GPCR target families (Figure 2D and Supplementary
Materials Figure S5). For Clozapine, the model predicted interactions with all five of its
known GPCR target families in addition to two unvalidated interactions with Orphan and
Melanin-concentrating receptors. For XAC and Naltrexone, the highest predicted probabil-
ity matched their known GPCR target families. Other predictions above the designated
probability threshold identified few additional unvalidated target families. Several of these
unknown interactions were later confirmed by NanoBRET ligand-engagement screens,
demonstrating the value of our machine learning-guided approach for scaffold selection
(see Results below). Together, these examples confirmed that the model could predict
known interactions of both selective and promiscuous GPCR ligands, making it a useful in
silico screening tool for identifying candidate scaffolds for development into NanoBRET
tracers. With that, we extended the model’s predictions to include binding profiles for a
total of 17 model scaffolds (Supplementary Materials Figure S6).

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2.7. Synthesis of Fluorescent Tracers from Selected Scaffolds
Once suitable scaffolds are selected, the next step toward generation of fluorescent trac-
ers is synthesis of their modifiable analogues. It is rather rare that a compound possesses an
easily modifiable group at a position that tolerates modification without impact on binding
properties. Often, introduction of a modifiable functionality (usually an amino-group for
amide coupling) requires either de novo total synthesis or application of sophisticated
late-stage chemistries. In addition, in many cases a site suitable for modification without
significant impact on binding properties is unknown, requiring empirical evaluation of
several sites. Below we highlight several examples taking advantage of different synthetic
strategies to generate modifiable analogues via a reasonable number of steps.
Clozapine represents an example of our approach evaluating a mixture of rational
and empirical modifications sites (Figure 3). Multiple SAR analyses for Clozapine and its
main targets suggest that position “a” might be suitable for fluorophore attachment [36].
This speculation is further supported by the structurally related atypical antipsychotic
drug Quetiapine having a PEG unit at position “a”. At the same time, it is possible that a
fluorophore appendage at this site might negatively impact the interactions of Clozapine
with some of its targets. With this in mind, we devised a strategy to generate three
Clozapine derivatives, each one with a different modifiable site (Figure 3). Clozapine-b,
containing a protected amino-methyl group for further modification through amide bond
formation, was generated via late-stage modification of commercially available Clozapine.
The two-step synthesis started with Ritter late-stage aromatic C−H functionalization [37]
followed by the attachment of a protected amino-methyl handle [38]. Both Clozapine-a
and Clozapine-c were synthesized from advanced intermediate C1, which was generated
via a reported 4-step synthetic scheme [33]. Reaction of C1 with appropriately substituted
piperazine delivered Clozapine-a. Similarly, Clozapine-c was generated by a two-step
sequence from C1.
Amitriptyline, a promiscuous tricyclic antidepressant [39], is another example of our
empirical exploration approach (structure and modifiable sites are portrayed in Figure 6A).
Nortriptyline, its commercially available demethylated analogue is amendable to facile
modification at its secondary amine. However, a recently published crystal structure
of dopamine transporter with bound Nortriptyline [40] suggests that the aromatic rings
of Amitriptyline/Nortriptyline might be more appropriate modification sites. From a
synthetic point of view, generating multiple modifiable analogues of Amitriptyline is
somewhat challenging due to its pseudo-symmetry, which may result in an inseparable
mixture of isomers. In addition, the presence of a reactive exocyclic double bond could
render many late-stage chemistries inapplicable. We sought to employ a recently reported
elegant approach to Amitriptyline’s skeleton [41], which could provide a straightforward
path to aryl-substituted Amitriptylines. Indeed, this approach delivered four amine-
reactive carboxy-modified Amitriptylines in a fast and expedient manner (Figure S7).
The modification of another scaffold AZD1283, a potent antagonist of the P2Y12 recep-
tor [42] is a good example of the synthetic advantages of late-stage modifications. A quick
inspection of AZD1283’s structure identified an ethyl ester as a synthetically accessible site
for modification and fluorophore attachment. However, both the reported SAR [42] and
analysis of P2Y12’s crystal structure in complex with AZD1283 ([43], PDB 4NTJ) suggested
that the phenyl ring on the opposite side of the molecule is a better candidate for modifica-
tion (structure and modifiable site are portrayed in Figure 6C). From a synthetic point of
view, late-stage modification of commercially available AZD1283 was especially attractive
considering the lengthy total synthesis of an AZD1283 analogue carrying a modifiable
group on its phenyl ring. Using late-stage alkenylation chemistry developed by Yu and
coworkers, [22] commercial AZD1283 was modified at the ortho position of the phenyl ring
(due to the directing effect of AZD1283’s sulfonamide), albeit at only 4% yield (Figure S8).
Despite the low yield, the unoptimized reaction delivered milligram quantity of modified
AZD1283, which was sufficient for subsequent development of fluorescent tracers. This
example illustrates the strategic advantage of applying late-stage chemistries to tracer

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discovery as it often allows generation of modifiable analogues in a faster and cheaper
manner than de novo synthesis.
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. (
intermediate C1 via reaction with 1-(3-N-Boc-propyl)-piperazine. (
C
) Boc-protected “Clozapine-a” was synthesized from advanced
) tBu-protected “Clozapine-c”
D
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.
Using a blend of different synthetic strategies similar to those outlined above, we
generated modifiable analogues for the 17 model scaffolds and conjugated them to a
fluorophore via amide-type couplings. We chose the red emitting NanoBRET 590 dye
(Eex 576 nm; Eem 589 nm) for a fluorophore due to its photochemical properties, including
small size, chemical stability, and sufficient spectral overlap with NanoLuc emission, which
is critical for efficient energy transfer [16] (Figure S9). The length and nature of the linker
connecting NanoBRET 590 to the modifiable scaffold can also influence the efficiency of
energy transfer as well as the physicochemical properties of the tracer (e.g., solubility). We
tuned the tracer’s properties with a set of three different linkers (Supplementary Materials
Figure S9). The shortest linker tested was a direct attachment of NanoBRET 590 to the
modifiable scaffolds. The two other linkers are a PEG-4 linker that provides extended
length between a molecular scaffold and NanoBRET 590 with an added benefit of increased
aqueous solubility and a linker comprising one PEG unit and a benzylamide unit, coupling
the solubilizing properties of PEG with the rigidity and hydrophobicity of a benzene ring.
We opted to first generate tracer candidates incorporating the PEG-4 linker to provide a
spacer between a scaffold and NanoBRET 590. When necessary, we further optimized the

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properties and binding affinity of these candidates by exchanging PEG-4 with other linkers.
We found empirically that incorporation of a hydrophobic moiety near NanoBRET 590 was
often beneficial to tracer performance. In the end, we generated several tracer candidates
for each one of the 17 scaffolds and report here the synthesis and evaluation of a selection
of the best-performing sets (Supplementary Materials Table S2 and Excel spreadsheet).
2.8. Evaluation of Binding Profiles for Fluorescent Tracers in a Cellular NanoBRET Screen
To evaluate the binding profile of each tracer candidate, we generated a library of
184 DNA constructs expressing GPCRs genetically fused to an N-terminal HiBiT. For
consistency and efficient translocation to the cell surface, native secretion signals were
removed, and an IL6 secretion signal sequence was incorporated upstream of HiBiT.
Additionally, to insure HiBiT integrity upon signal peptide removal, a two amino acid
valine–serine spacer was inserted between the signal sequence and HiBiT [5].
Each tracer candidate was screened across the entire library of HiBiT-GPCR constructs
using NanoBRET as the detection modality. As depicted in Figure 4A, the specificity of
interactions was evaluated through decrease in BRET due to competitive displacement
of a bound tracer by excess parental compound. Each tracer was analyzed for binding
to each HiBiT-GPCR fusion in the absence and in the presence of competing parental
compound. Briefly, HEK293 cells were transfected with HiBiT-GPCR constructs diluted
into promoterless DNA to reduce expression and maximize translocation to the cell surface.
Each tracer candidate was then screened at concentrations of 1
expressing these fusions. Cells were treated for 1.5 h with tracer either alone or in the
presence of 20 M competing parental compound (i.e., control). BRET was measured upon
µM and 0.1 µM across cells
µ
addition of purified LgBiT and subsequent complementation with HiBiT genetically fused
to a GPCR. The specificity of interactions was assessed through a decrease in BRET signal
due to competitive displacement of the bound tracer. To further gauge the robustness
of these interactions, the fold response over the control (i.e., BRETtracer/BRETcontrol) was
calculated. We chose a 1.5-fold response, which translates to
≥
7-fold signal over noise as
the minimal threshold required to score an interaction as positive. The compiled screening
results for all fluorescent tracer candidates across 184 GPCRs from 51 different families and
their structures are included in the Supplementary Materials (Excel spreadsheet). Below
we highlight the screening results obtained for our exemplary fluorescent tracers based on
Clozapine, Amitriptyline and AZD1283.
Analyses of all three Clozapine tracers revealed specific interactions with 19 GPCRs
from the five expected families, albeit with a different binding profile for each one (Figure 4B).
These tracers were generated by conjugation of NanoBRET 590 to modifiable Clozapines
(Clozapine a–c; Figure 3) via a PEG4 linker (linker 2; Figure S9). Clozapine-c-2-NB590,
modified on the diazepine’s nitrogen, had a poor binding profile that was limited to five
GPCRs at the higher 1
NB590, modified on the phenyl ring, was broadened to 18 GPCRs. However, only half of
them retained a 1.5-fold response at the lower 0.1 M tracer concentration. Clozapine-a-
µM tracer concentration. The binding profile for Clozapine-b-2-
≥
µ
2-NB590, modified on the piperazine ring, had the broadest binding profile. It exhibited
specific binding interactions with all 19 GPCRs and retained with 17 of them a ≥1.5-fold
response at the lower 0.1 µM tracer concentration. Altogether, this analysis showed
that modification of the piperazine ring, which is supported by SAR [36], is tolerant to
attachment of a fluorophore.

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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 (
from five different families. Clozapine-a-2-NB590 had the broadest binding profile.
≥
1.5-fold response) with GPCRs
Encouraged by the broad binding profile of Clozapine-a-2-NB590 (Figure 5A), it was
further evaluated for concentration dependent binding to the 19 GPCRs identified as
interactors (Figure 5B–F). HEK293 cells expressing the 19 HiBiT-GPCRs were treated with
increasing concentrations of Clozapine-a-2-NB590 alone or in the presence of competing
Clozapine (30 µM). Background corrected BRET measurements showed robust, specific
and concentration-dependent binding for 17 of the 19 GPCRs, exhibiting a wide range
of EC50 values (1–600 nM). Concentration-dependent binding to the remaining GPCRs,
ADRA2A and HTR1D, was specific but suffered from high background, likely due to
nonspecific BRET originating from random interactions. Interestingly, in the NanoBRET
screen, interactions between Clozapine-a-2-NB590 and these 17 GPCRs retained a ≥1.5-fold
response at the lower 0.1 µM concentration while interactions with ADRA2A and HTR1D
did not. This suggests a correlation between the retention of a
the lower 0.1 M tracer concentration and the likelihood for a robust saturation ligand-
engagement assay.
≥
1.5-fold response at
µ

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Figure 5. Saturation binding of Clozapine-a-2-NB590 to HiBiT-GPCRs from five families. (
A) Structure of Clozapine-a-2-
NB590. ( ) Specific dose-dependent BRET measurements for HiBiT-GPCRs expressed in HEK293 cells. BRET values at
B–F
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.
The benefit of evaluating several modification sites was further demonstrated for
Amitriptyline where we observed significantly different binding profiles among the five
tracer candidates (Figure 6A,B). These tracer candidates were generated by conjugation
of NanoBRET 590 to modifiable Amitriptylines (Figure 6A; Amitriptyline a–e) via a PEG4
linker (linker 2). Amitriptyline-e-2-NB590, modified on the amine nitrogen of commercial
Nortriptyline, resulted in a poor tracer with no specific interactions. Although the other
four tracer candidates were generated through modification of positions just one atom
apart on the two quasi-symmetric phenyl rings, only one of them provided a substantially
broad binding profile. Amitriptyline-b-2-NB590 displayed specific binding interactions to
17 GPCRs from seven different families, with 16 of them exhibiting a
the 0.1 M tracer concentration. The binding profiles of the other three candidates were
more limited with detectable interactions predominately at the 1 M tracer concentrations.
≥
1.5-fold response at
µ
µ
For AZD1283, the third tracer scaffold, late-stage alkenylation enabled conjugation of
NanoBRET 590 to the phenyl ring with minimal synthetic effort (Figure 6C,D). NanoBRET
screen revealed selective binding to the Purinergic receptor family (Figure 6D), which was
in agreement with the machine learning predictions. Interestingly, in addition to the known
interaction with P2RY12, the screen uncovered two previously unreported interactions
with P2RY8 and P2RY13. Notably, all three interactions exhibited a
the two tracer concentrations tested.
≥
1.5-fold response at

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Figure 6. NanoBRET screen of Amitriptyline and AZD1283 tracer candidates. (
positions marked. ( ) NanoBRET screen across 184 HiBiT-GPCRs from 51 families revealed specific interactions (
response) with GPCRs from seven families. Amitriptyline-b-2-NB590 had a substantially broader binding profile exhibiting
specific interactions with 17 GPCRs. ( ) Structure of AZD1283 with modifiable position marked. ( ) NanoBRET screen
revealed specific interactions (≥1.5-fold response) with 3 Purinergic GPCRs.
A
) Structure of Amitriptyline with modifiable
B
≥
1.5-fold
C
D
Altogether, the NanoBRET screen of tracer candidates for 17 different scaffolds uncov-
ered specific binding interactions for 58 GPCRs from 18 different families (Figure S10). The
screen identified 160 individual interactions with 77% of them retaining
≥
1.5-fold response
at the 0.1 M tracer concentration. Notably, several of those verified interactions, which
µ
were not described before (30%), uncovered unexpected and unreported promiscuity for
well-studied GPCR ligands considered to be selective such as Aminopotentidine, NAN-190,
XAC and Tolterodine.
2.9. Comparing Machine Learning Predictions and Empirical NanoBRET Evaluations
Finally, we opted to compare the machine learning predictions for the 17 scaffolds
to the empirical NanoBRET evaluations across 36 GPCR families investigated by both
strategies (Figure 7). The highest correlation between model-predicted and NanoBRET-
confirmed interactions was found using minimal thresholds of
≥
4% classification prob-
ability and 1.5-fold response, respectively (Figure S11). These thresholds provided the
≥
best balance between model accuracy and a low percentage of false predictions. Accord-
ingly, machine learning predictions for all unmodified scaffolds were compiled (Figure S6)
and those above the minimum 4% probability threshold were classified as positive in-
teractions (Figure 7A). At the same time, empirical NanoBRET results for each scaffold
were consolidated to include results for all relevant fluorescent tracer candidates screened
at either 1
µM or 0.1
µM tracer concentrations. Screening results were further grouped
by families and considered positive if at least one member had a
≥
1.5-fold NanoBRET

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response (Figure 7B). One caveat comparing model predictions to empirical NanoBRET
evaluations is their use of unmodified versus fluorescently modified scaffolds, respectively.
Therefore, model predictions not verified by BRET could in part be the result of fluorophore
interference with binding interactions. Where feasible, we sought to address this possibility
and further verify interactions for unmodified scaffolds via competitive displacement of
other fluorescent tracers already shown to bind the relevant targets. For example, using
Clozapine-a-2-NB590 we were able to verify additional interactions for Aminopotentidine
with alpha-Adrenergic, Dopamine and 5-Hydroxytryptamine receptors as well as addi-
tional interactions for XAC with alpha-Adrenergic receptors. These additional verifications
were incorporated to the comparative analysis and are also included in the compiled
screening results (Supplementary Materials Excel spreadsheet).
Figure 7. Machine learning predictions for GPCR-ligand interactions versus empirical NanoBRET screens. (
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. ( ) 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. ( ) Concordance between machine learning predictions and NanoBRET
screens. Confirmed predictions are shown in purple.
A) Machine
≥
B
≥
C
Overlap analysis between machine learning predictions and empirical NanoBRET
evaluations (Figure 7C) revealed that 568 out of the total 612 predictions were confirmed
by BRET (i.e., 92.8% accuracy). However, given the high number of negative interactions
that were predicted and confirmed, this accuracy value is likely biased toward a high
value. Indeed, 512 of the 530 (97.2%) negative interactions and 56 of the 82 (68.3 %) positive
interactions predicted by the model were confirmed by NanoBRET. The corresponding
specificity (94.9%) and sensitivity (76.7%) metrics demonstrate the overall effectiveness of
this workflow for tracer discovery.
2.10. Summary
The high specificity of NanoBRET is particularly attractive when combined with
the versatility of promiscuous fluorescent tracers, alleviating the burden of developing a
specific tracer for each target. Here, we developed an integrated tracer discovery workflow
coupling machine learning-guided in silico screening for promiscuous tracer scaffolds
with a blend of synthetic strategies to rapidly generate multiple tracer candidates. Using

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this workflow, we developed over a dozen fluorescent tracers that can effectively engage
several GPCRs from one or more families. A limited set of 10 leading tracers enabled robust
saturation ligand-engagement assays for 40 GPCRs, including some overlapping coverage
among them (Figure 8). Our screen uncovered interactions with 11 other GPCRs that
retained a
≥
1.5-fold response at lower 0.1 µM tracer concentrations. Further optimization
of the associated tracers’ linker chemistry or conjugation site could expand this coverage,
particularly for GPCRs that naturally bind peptides and polypeptides, but was beyond the
scope of this study. We believe that our strategy has the potential to accelerate fluorescent
tracer discovery and reduce the synthetic burden associated with their development.
Furthermore, this approach could be applied to other target classes beyond GPCRs.
Figure 8. NanoBRET GPCR ligand-engagement assays facilitated by lead fluorescent tracers. A circu-
lar 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.
3. Materials and Methods
Supplementary Materials includes additional details regarding reagents, DNA con-
structs, cell culture, transfection, and NanoBRET saturation ligand-engagement assay.
3.1. Data Collection and Pre-Processing for Machine Learning
Chemical interaction data was downloaded from the GLASS database (https://
zhanglab.ccmb.med.umich.edu/GLASS/) in 3 October 2018. All data processing was per-
formed in R version 3.6.3. The raw interaction data was filtered to select compounds that

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target human GPCRs, have an assigned CHEMBL ID number, and their chemical structure
represented in SMILES format [44]. The resulting dataset had a total of 304,564 target-
ligand interactions across 230 GPCRs. To facilitate analysis of the data structure and reduce
the number of classes required for machine learning each interaction was further assigned
into an established GPCR family based on its target receptor [1]. The dataset contained
56,902 unique compounds, with the most frequent molecule occurring 120 times. To reduce
biases in target-ligand interaction data and maximize the chemical diversity available for
machine learning, data was filtered for unique compound-GPCR family combinations,
resulting in a “Filtered” dataset of 136,848 unique molecular interactions with 227 GPCRs.
In the resulting dataset, several GPCR families had only a small number of described
interactions, therefore, only families having interactions with at least 700 unique com-
pounds were selected for further analysis. This minimum threshold was chosen to balance
molecular diversity against total number of training examples for each family.
A final “Selected” dataset was created by random selection of 530 target-ligand in-
teractions for each GPCR family from the Filtered dataset, preserving equal class balance
across family categories. The Selected dataset had 23,850 unique target-ligand interactions
distributed equally among 45 GPCR families. This dataset was randomly split 70/30 into
training and test sets where each received 16,695 and 7155 interactions, respectively. After
conversion of compounds from SMILES format to molecular fingerprints, the training set
was used as input to train the machine learning model while the test set was held out for
subsequent validation of the final model.
3.2. Data Representation using Molecular Fingerprints
A combination of three molecular fingerprinting strategies was used to capture dif-
ferent representations of each compound in our dataset and maximize the information
available to the machine learning algorithm during training. Compounds in SMILES
format were converted to a single numerical vector of 758 fingerprints comprising 166-bit
MACCS keys [26], 79-bit complete E-State atom types [27], and 512-bit Open Babel FP4
fingerprints [45] using the RCPI package version 1.20.1 in R [46].
Since the time required to train machine learning models can be lengthy and is partially
dependent on the number of features representing the data, the number of fingerprints was
reduced by removing those that were either unused or had low variance in our training
dataset. This allowed for faster model training and improved the model’s ability to fit the
data by protecting against highly imbalanced features that might exert undue influence
during training. Using the caret package version 6.0-86 in R [47] near-zero variance features
were eliminated, resulting in a reduction from 758 to 187 fingerprints representing com-
pounds in the training and test datasets. Molecular information represented as fingerprints
was then used as the input for training the machine learning model.
3.3. UMAP Clustering of Molecules in the Training Dataset
Uniform Manifold Approximation and Projection (UMAP) is a general-purpose man-
ifold algorithm for dimensional reduction [29]. UMAP occurs in two phases. First, a
weighted k-nearest neighbor graph is computed on features of the dataset and second, a
low-dimensional layout of the graph is calculated. The resulting embedded datapoints
preserve the high-dimensional structure of the data but allow it to be visualized in lower
dimensions and make comparisons with other variables of interest.
The MACCS molecular fingerprint representation of the training dataset was used as
input and the graph was computed using the UMAP package version 0.2.5.0 in R. Since the
resulting spatial distribution following UMAP dimensionality reduction is dependent on
the user-defined number of nearest neighbors, a range of settings from 10–400 neighbors
were explored. A value of 50 nearest neighbors provided the optimal spread of the data
in low dimensions suitable for visualization while retaining the clustering of compounds
according to their target GPCR families. All UMAP visualizations were created using the
ggplot2 package version 3.3.0 in R [48].

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3.4. Training the Machine Learning Model
Machine learning model training was carried out using the caret package version
6.0-86 in R. Employing the training set in molecular fingerprint format as the input, a
random forest classification model was trained for accuracy using 5-fold cross-validation
repeated 10 times. Software defaults were used for automatic data centering, scaling, and
hyperparameter tuning during the training phase (see Supplementary Materials). The
training process created a model fitting target classes (GPCR families) as a function of the
molecular fingerprints of their interacting compounds, using their annotation from the
GLASS database as the ground truth.
Performance metrics such as accuracy, sensitivity, and specificity were calculated auto-
matically from model predictions of target GPCR family for compounds in the test set using
their specific interactions in the GLASS database as ground truth. Using measurements
for true positive (TP), true negative (TN), false positive (FP), and false negative (FN), the
formulas used were Accuracy = (TP + TN)/(TP + FN + TN + FP), Sensitivity = TP/(TP
+ FN), and Specificity = TN/(TN + FP). Statistics at the GPCR-family level were used to
create a confusion matrix evaluating the accuracy of model prediction on target-ligand
interactions for individual families. All statistical metrics were calculated automatically
following model training using caret in R.
3.5. Machine Learning Model Implementation and Comparison with NanoBRET Data
To predict the GPCR target family for compounds used as scaffolds for fluorescent
tracer development, the chemical structure of each unmodified compound was converted to
SMILES representation using ChemDraw Professional version 16.0. Molecular fingerprints
were extracted as outlined earlier in Methods using the RCPI package in R. The machine
learning model was imported into an R environment and used to predict the GPCR family
class probabilities for each scaffold.
Class probabilities, converted to values ranging from 0 to 100% for readability, were
used as a guide to search for potential interactions among compounds and GPCR families.
A threshold of 4% and above for positive interactions provided the best correlation between
the machine learning predictions and empirical NanoBRET data.
3.6. Robotic NanoBRET Screening
HiBiT-GPCR DNA constructs were diluted in 10 mM Tris pH 8 to a final concentration
of 0.01
µ
g/
µ
L, arrayed in 96-well plates and stored at
−
20 ◦C. For transfection, HEK293
cells were suspended in Opti-MEM (without phenol red) supplemented with 2% fetal
bovine serum (Seradigm) and 100 units/mL penicillin–streptomycin (Gibco) at a final
concentration of 220,000 cells/mL and dispensed into white 96-well plates at 90 µL/well.
Subsequent transfections and screens were carried using an Agilent Bravo Automated
Liquid Handling Platform. First, the 96-well HiBiT DNA arrays were diluted 25-fold in
Opti-MEM (without serum or phenol red) to a final concentration of 0.0004
20 L of the diluted DNAs were transferred into new 96-well plates. Next, promoterless
carrier DNA (1 g/ L) was diluted 25-fold (0.04 g/ L) and then dispensed into the
diluted arrays at 20 L/well. Following brief mixing, Viafect transfection reagent (Promega)
diluted 16-fold into Opti-MEM was dispensed into the plates at 40 L/well. After mixing
µg/µL and
µ
µ
µ
µ
µ
µ
µ
by pipetting, plates were incubated for 10 min on the pipetting station deck. The DNA-
transfection reagent complexes were then added to plates containing cells. To facilitate
the screen, 10 µL of DNA-transfection reagent complexes were added in duplicates to the
top 4 and bottom 4 rows, resulting in two identical◦48-well arrays per plate. Plates with
transfected cells were incubated for 18–20 h at 37 C/5% CO2. A screen of each tracer
across the entire library using two different concentrations requires duplicate plates of
transfected cells. Depending on the number of tracers being screened, transfections can be
scaled up using the same ratios. The next day, for each tracer, four 20
in tracer dilution buffer (Promega) were prepared: two duplicates at final concentration
of 20 M and two duplicates at a final concentration of 2 M. A competing unmodified
×
screening solutions
µ
µ

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compound was added to one of each duplicate at a final concentration of 400
µM. The
four resulting solutions were diluted 4-fold in Opti-MEM without serum or phenol red
to generate 5 solutions (5 M and 0.5 M tracer +/ 100 M competing compound).
To screen the library, a BioTek MultiFlo FX liquid dispenser was used to dispense 22.5
of tracer solution to the top 4 rows and 22.5 L tracer + competing compound solution
to the 4 bottom rows. Plates were briefly mixed on an orbital shaker and incubated for
1.5 h at room temperature. The MultiFlo FX liquid dispenser was used to dispense 2
detection reagent (100-fold dilution of LgBiT (Promega) and 50-fold dilution of furimazine
live cell substrate (Promega) in Opti-MEM) at 125 L/well. Plates were briefly mixed on an
×
µ
µ
−
µ
µL
µ
×
µ
orbital shaker and incubated for 15 min to allow complementation. Filtered luminescence
was measured using a GloMax Discover microplate reader (Promega) equipped with a
450-nm (8-nm band pass) filter (donor) and a 600-nm long pass filter (acceptor). BRET
was calculated by dividing the acceptor emission by the donor emission. Fold response
was calculated for each construct at each tested tracer concentration by dividing BRET
from wells treated with tracer alone (BRETtracer) by BRET from duplicate wells treated with
tracer + competing compound (BRETcontrol). The minimal threshold for specific interactions
was 1.5-fold response, which translates to
calculated for each interaction in a limited set of triplicate screens using the formulation:
≥
7-fold signal over noise. Signal over noise was
Signal/noise = (BRETtrace − BRETcontrol)/STDEV(BRETcontrol
)
(1)
Supplementary Materials: The following are available online. Supplementary Methods, Figures
and Tables, Synthetic procedures and Compound characterization (PDF); NanoBRET screen results
(Excel); Glass dataset (csv); Machine learning script (R).
Author Contributions: Conceptualization, M.P.K., S.L., M.E.B. and R.F.O.; methodology, M.P.K., S.L.,
M.E.B. and R.F.O.; software, M.P.K.; validation, M.P.K., M.E.B. and R.F.O.; formal analysis, M.P.K.,
S.L., M.E.B., K.Z., R.H., M.P.H. and R.F.O.; investigation, M.P.K., S.L., M.E.B., K.Z., R.H. and R.F.O.;
data curation, M.P.K., M.E.B. and R.F.O.; writing—original draft preparation, M.P.K., S.L., T.M., T.A.K.
and R.F.O.; writing—review and editing, M.P.K., S.L., T.M., T.A.K. and R.F.O.; All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study is openly available and is included as
part of the Supplementary Materials.
Acknowledgments: We thank Keith V. Wood for useful discussions, Barrie Kellam and Sarah Mistry
for assistance with synthetic chemistry and Emily R. Lackner for assistance with generation of
DNA constructs.
Conflicts of Interest: All authors were Promega employees at time of manuscript preparation. The
authors declare no conflict of interest.
Sample Availability: Samples of the compounds are not available from the authors.
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