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. 2025 Jul 7;16(8):1554–1561. doi: 10.1021/acsmedchemlett.5c00171

One Tracer, Dual Platforms: Unlocking Versatility of Fluorescent Probes in TR-FRET and NanoBRET Target Engagement Assays

Erika Y Monroy †,‡, Xin Yu †,‡, Dong Lu †,‡, Xiaoli Qi †,‡, Jin Wang †,‡,§,*
PMCID: PMC12315041  NIHMSID: NIHMS2096549  PMID: 40756692

Abstract

Target engagement assays are essential for drug discovery, utilizing time-resolved fluorescence resonance energy transfer (TR-FRET) and nano bioluminescence resonance energy transfer (NanoBRET) as complementary methods for biochemical and cellular evaluation. Traditional platforms require distinct fluorescent tracers, increasing costs, and complexity. This study systematically evaluates the cross-platform performance of T2-BODIPY-FL and T2-BODIPY-589, tracers developed for receptor-interacting protein kinase 1 (RIPK1) target engagement in TR-FRET and NanoBRET applications, respectively. Our results demonstrate that both tracers effectively bridge biochemical and cellular assays, providing reliable measurements. T2-BODIPY-589 demonstrates superior performance in NanoBRET (Z′ up to 0.80) and acceptable functionality in TR-FRET (Z′ = 0.53). Conversely, T2-BODIPY-FL performs optimally in TR-FRET (Z′ = 0.57) and exhibits NanoBRET potential (Z′ up to 0.72). Competition assays with an unlabeled inhibitor yielded consistent binding constants across all combinations. These findings suggest that a single tracer can integrate diverse assay platforms, enhancing consistency and comparability in drug discovery.

Keywords: fluorescent tracer, TR-FRET, NanoBRET, target engagement, high-throughput screen, Z′ factor, RIPK1, BODIPY

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Introduction

Biomedical research has accelerated significantly in recent decades, driven by advances in biochemical, cellular, and tissue-based assays. These advancements expedite new therapeutic discovery by elucidating crucial protein–protein interactions for effective treatment strategies. While methods like surface plasmon resonance, thermal shift assays, nuclear magnetic resonance, and various calorimetry techniques measure direct target–compound interactions, many face limitations, such as low throughput, protein purification requirements, or high noise level. −

Fluorescence/luminescence-based technologies are critical tools in modern molecular biology and drug discovery, particularly for high-throughput screening. Their advantages include high sensitivity, signal stability, adaptability across different assay formats, operational simplicity, and non-destructive analysis. − Their versatility makes them indispensable across scientific disciplines, from detecting viral proteins in infected cells to screening diverse compound libraries against challenging therapeutic targets. ,

Among these technologies, time-resolved fluorescent resonance energy transfer (TR-FRET) and nano bioluminescent resonance energy transfer (NanoBRET) have gained prominence. Both non-invasive methods are mainstays in drug discovery for measuring protein–ligand interactions due to their high sensitivity and low background noise, leading to their growing popularity in preclinical biomedical research. , Assay performance assessment using hit confirmation criteria has been extensively studied by Zhang and colleagues and is summarized in Table S1.

TR-FRET combines fluorescence sensitivity with FRET specificity to study molecular interactions. This technique relies on energy transfer between a donor (typically a lanthanide chelate) and an acceptor fluorophore when they are in close proximity (1–10 nm). TR-FRET utilizes donor fluorophores with exceptionally long fluorescent lifetimes (microseconds), enabling time-resolved detection that minimize background fluorescence and enhance signal-to-noise ratios. − TR-FRET assays allow quantitative measurements of protein–ligand interactions, integrating seamlessly into high-throughput screening platforms for drug discovery. −

NanoBRET, developed by Promega Corporation, leverages bioluminescent resonance energy transfer to monitor target–ligand interactions in real time under physiologically relevant conditions. , At its core, NanoBRET employs NanoLucⓇ, a compact 19 kDa luciferase enzyme. NanoLuc emits light upon catalyzing its substrate furimazine, generating a bioluminescent signal that excites nearby acceptor fluorophores within a critical 1–10 nm proximity range. Unlike TR-FRET, NanoBRET’s spontaneous light generation from enzymatic catalysis eliminates several technical challenges, minimizing autofluorescence and photobleaching while significantly reducing background noise. This technique is invaluable for tracking molecular interactions as they occur within the complex environment of living cells, providing insights into target engagement dynamics by small molecules and biologics under conditions that closely mirror the native cellular context. −

Fluorescent tracers are crucial components in both TR-FRET and NanoBRET assays, though their spectral requirements differ significantly between platforms. Terbium-based TR-FRET assays typically select fluorophores absorbing at 488 nm, leveraging the lanthanide donor’s characteristic emission bands. − Conversely, NanoBRET assays use red-shifted fluorophores, such as BODIPY 576/589 or NCT derivatives, as acceptors. These are chosen for their minimal spectral overlap with NanoLuc donor emission to optimize energy transfer efficiency and reduce the background signal.

Drug discovery campaigns often require both TR-FRET and NanoBRET platforms for the comprehensive characterization of compound–target interactions from biochemical to cellular contexts. This dual requirement traditionally necessitates synthesizing separate tracers conjugated with platform-specific fluorophores, increasing development costs and potential variability between assay formats.

In this study, we evaluate the cross-platform compatibility of tracers originally designed for a single assay type. Specifically, we assess whether a TR-FRET-optimized tracer can effectively function in NanoBRET applications and vice versa. Using two fluorescent tracers, T2-BODIPY-FL (T2-BDP-FL) and T2-BODIPY-576/589 (T2-BDP-589), initially developed for RIPK1 target engagement studies, we systematically characterize their performance across both platforms under various detection parameters and assay conditions. ,,, These tracers demonstrate versatility across biochemical and cellular assay formats. Through comprehensive comparative analysis, we show that thoughtfully designed fluorescent tracers can bridge the gap between in vitro and cellular assay systems, delivering consistent and biologically relevant data while streamlining the tracer development process for multiplatform drug discovery applications.

For clarity, the BODIPY 576/589 (BDP589) dye used for tracer development in this work has the same chemical structure as Promega’s NanoBRET 590 (NB590). The difference is only in nomenclature.

Results

Photophysical Characterization

For comprehensive target engagement studies across biochemical and cellular platforms, we previously developed two specialized fluorescent tracers: T2-BDP-FL (Figure A) for TR-FRET and T2-BDP-589 (Figure B) for NanoBRET assays.

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Chemical structures of fluorescent tracers used for cross-platform evaluation. (A) T2-BODIPY-FL (T2-BDP-FL) and (B) T2-BODIPY-576/589 (T2-BDP-589) were employed for RIPK1 target engagement assays. (C) K5-BODIPY-FL (K5-BDP-FL) and (D) K5-BODIPY-576/589 (K5-BDP-589) were used for BTK assays.

In TR-FRET assays, terbium (Tb) serves as an ideal donor due to its long fluorescence lifetime (milliseconds vs nanoseconds), enabling time-gated detection and improved signal-to-noise ratios. , Upon excitation at 340 nm, Tb exhibits four major emission transitions from the 5D4 excited state to various 7FJ ground states (J = 6, 5, 4, and 3), producing characteristic narrow emission bands at approximately 490, 548, 587, and 620 nm. The first transition (5D4 → 7F6) at 490 nm overlaps well with T2-BDP-FL’s excitation spectrum (Figure A), ensuring high energy transfer efficiency. Conventional TR-FRET assays with this pair typically use 520 ± 10/490 ± 10 nm filter settings, capturing T2-BDP-FL emission while effectively discriminating against the Tb signal.

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Spectral characterization of fluorescent tracers with TR-FRET and NanoBRET donors and optimized detection windows. (A)B Emission spectrum of terbium (Tb) overlaid with the excitation and emission spectra of BODIPY-FL. The 490 ± 10 nm (excitation) and 520 ± 10 nm (emission) filters are optimal for TR-FRET detection using this donor–acceptor pair.(B) Emission spectrum of terbium (Tb) overlaid with the excitation and emission spectra of BODIPY-589. The 490 ± 10 nm and 640 ± 10 nm or 490±10 nm and 550±25 nm filters are optimal for TR-FRET detection using this donor–acceptor pair. (C) Emission spectrum of NanoLuc overlaid with the excitation and emission spectra of BODIPY-FL, showing respective filter bandwidths for donor and acceptor channels in NanoBRET detection. (D) Overlay of NanoLuc emission with BODIPY-589 excitation and emission spectra, showing filter bandwidths for donor and acceptor channels used in NanoBRET detection. The spectral separation minimizes donor bleed-through and enhances signal resolution.

Interestingly, Tb’s second (5D4 → 7F5 at 548 nm) and third (5D4 → 7F4 at 587 nm) transitions also overlap with T2-BDP-589’s excitation spectrum (Figure B). This compatibility suggests T2- BDP-589’s potential versatility in TR-FRET assays, optimizable with alternative filter settings like 640 ± 10/490 ± 10 or 640 ± 10/550 ± 25 nm to capture its red-shifted emission while maintaining Tb discrimination.

In NanoBRET assays, NanoLuc luciferase generates a blue luminescent signal with a maximum emission at 460 nm, tapering to approximately 600 nm. T2-BDP-FL’s excitation spectrum significantly overlaps with this NanoLuc emission profile (Figure C), theoretically enabling energy transfer in cellular systems. However, this spectral proximity can cause signal bleed-through from the intense donor into the acceptor channel. To address this, we evaluated various emission settings using a linear variable filter (LVF) monochromator detector, incrementally adjusted in 30 nm bandwidths, alongside a 610 nm long-pass filter. This identified optimal detection parameters maximizing the T2-BDP-FL signal while minimizing NanoLuc bleed-through. Similarly, we assessed T2-BDP-589’s performance (Figure D), which was designed with red-shifted spectral properties to enhance separation from the NanoLuc emission and reduce cellular assay interference.

This comprehensive photophysical characterization of both tracers across multiple detection parameters aimed to determine their versatility across TR-FRET and NanoBRET platforms, potentially simplifying assay development and increasing experimental flexibility in target engagement studies.

Optimizing TR-FRET: Cross-Platform Utility of T2-BDP-FL and T2-BDP-589 for RIPK1 Target Engagement

To evaluate the cross-platform compatibility, we optimized TR-FRET assay conditions using T2-BDP-FL and T2-BDP-589 with His-tagged RIPK1 and an anti-6× His-Tb antibody. Three filter pair combinations (520 ± 10/490 ± 10, 640 ± 10/490 ± 10, and 640 ± 10/550 ± 25 nm) were tested to determine the optimal detection parameters.

Results, summarized in Table S2, revealed distinct performance profiles. T2-BDP-FL performed best with the 520 ± 10/490 ± 10 nm filter pair (Z′ = 0.57 and signal window = 6.0), consistent with its spectral properties and TR-FRET compatibility but showed negative Z′ values with other combinations. T2-BDP-589, designed primarily for NanoBRET, demonstrated adequate TR-FRET performance with the 640 ± 10/490 ± 10 nm filter pair (Z′ = 0.53 and signal window = 7.8) and acceptable performance with the 520 ± 10/490 ± 10 nm filter pair (Z′ = 0.40 and signal window = 10.5). However, its performance was marginal with the 640 ± 10/550 ± 25 nm filter pair (Z′ = 0.29 and signal window = 3.1).

Dose–response analysis (panels A–C of Figure ) confirmed both tracers’ functionality in TR-FRET. T2-BDP-FL exhibited an equilibrium dissociation constant (K d) of 608 nM with the 520 ± 10/490 ± 10 nm filter pair, while T2-BDP-589 showed a K d of 443 nM with the 640 ± 10/490 ± 10 nm filter pair. Although other filters (520 ± 10/490 ± 10 and 640 ± 10/550 ± 25 nm) met minimal assay feasibility, only conditions with Z′ values exceeding 0.5 were suitable for subsequent IC50 determination with the unlabeled T2 inhibitor. This optimization establishes a robust and sensitive TR-FRET assay for RIPK1, ensuring reliable characterization of binders.

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Optimization of the TR-FRET assay for RIPK1 target engagement. (A–C) Saturation binding and non-specific binding curves for 1 nM His-hRIPK1 enzyme with increasing concentrations of (A) T2-BODIPY-FL and (B and C) T2-BODIPY-589 under different TR-FRET filter combinations. (D) Competition binding curves of the unlabeled T2 inhibitor against His-hRIPK1 using optimized concentrations of T2-BODIPY-FL and T2-BODIPY-589 in TR-FRET assays. Data represent the mean ± SD (n = 6 technical replicates).

In Figure D, we apply optimized filter settings to assess the unlabeled T2 inhibitor’s relative affinity, a highly selective RIPK1 inhibitor. To accurately determine the inhibitory constant (K i), we selected fluorescent tracer concentrations at their K d values using the Cheng–Prusoff equation (K i = IC50/2). Interestingly, competition assays with the unlabeled T2 inhibitor (Table S3) yielded K i values of 18 ± 0.70 nM for T2-BDP-FL and 54 ± 5.9 nM for T2-BDP-589. This discrepancy likely arises from differences in detection parameters rather than true binding affinity variations, as filter settings capture distinct emission regions, affecting the signal intensity and binding metrics. Both tracers were effectively displaced by the unlabeled T2 inhibitor, demonstrating a specific binding to RIPK1.

While the T2-BDP-589 probe allows for two possible filter settings, the T2-BDP-FL filter settings (520 ± 10/490 ± 10 nm) better aligned with the TR-FRET assay’s emission spectrum, providing a more sensitive measure of unlabeled T2 inhibitory activity and ensuring greater consistency. These findings confirm T2-BDP-FL as the optimal choice for TR-FRET applications, while T2-BDP-589 demonstrates versatility and potential for cross-platform use with the appropriate detection parameters. The generalizability of these optimized TR-FRET conditions was further supported by studies with Bruton’s tyrosine kinase (BTK), detailed in Figures S1 and S2 and Tables S4 and S5.

Cross-Platform Tracer Versatility: T2-BDP-FL and T2-BDP-589 in NanoBRET RIPK1 Target Engagement

To assess target versatility in cellular contexts, we evaluated T2-BDP-FL and T2-BDP-589 in NanoBRET assays using NanoLuc-tagged RIPK1 expressed in HEK293T cells. We employed a LVF monochromator detector to investigate various emission combinations and establish optimal detection parameters, adjusting the monochromator’s 30 nm bandwidth intervals (Table S6).

T2-BDP-589 demonstrated strong performance across various emission pairs using a LVF monochromator and filter optical configurations. The highest Z′ factor (0.80) and signal window (15) were achieved with the 610 nm long-pass filter and the 460 ± 30 nm monochromator setting. The 610 ± 30 and 460 ± 30 nm settings yielded a Z′ factor of 0.79 and a signal window of 17. In contrast, the 640 ± 30 and 460 ± 30 nm combination resulted in a Z′ factor of 0.58 and a signal window of 7.7, while the 580 ± 30 and 460 ± 30 nm setting yielded a Z′ factor of 0.62 and a signal window of 7.6. These results confirm that T2-BDP-589 is well-suited for NanoBRET applications, exhibiting robust assay performance across multiple detection parameters.

Despite being optimized for TR-FRET, T2-BDP-FL also demonstrated effective NanoBRET compatibility, particularly with the 520 ± 30 and 460 ± 30 nm monochromator settings (Z′ = 0.72 and signal window = 14). However, performance declined with the 610 nm long-pass filter and the 460 ± 30 nm setting (Z′ = 0.21 and signal window = 3.1), likely due to insufficient emission from T2-BDP-FL at longer wavelengths.

Dose–response analysis provided further insights into tracer binding affinities. T2-BDP-FL displayed K d values ranging from 619 to711 nM across different monochromator and filter combinations (panels A-C ofFigure ). T2-BDP-589 exhibited lower K d values, ranging from 232 to 267 nM (panels D–F of Figure ), demonstrating strong and specific binding. While both tracers showed reduced performance when shifting the acceptor wavelength, T2-BDP-589 maintained superior overall reliability and assay performance. These optimizations are essential for accurate target engagement quantification, facilitating reliable NanoBRET assay results.

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Validation of tracer compatibility for enhanced NanoBRET assay performance. (A–C) Binding and non-specific binding interactions of T2-BODIPY-FL to nLuc-hRIPK1 in HEK293T cells under different optical configuration settings: (A) 520 ± 30/460 ± 30 nm, (B) 550 ± 30/460 ± 30 nm, and (C) 580 ± 30/460 ± 30 nm. (D–F) Total and non-specific binding of T2-BODIPY-589 to nLuc-hRIPK1 in HEK293T cells with optical configuration settings of (D) 610 ± 30/460 ± 30 nm, (E) 610 nm LP/460 ± 30 nm, and (F) 640 ± 30/460 ± 30 nm, illustrating the effect of varying concentrations on binding efficacy. Data in panels A–F are presented as the mean ± SD (n = 4 technical replicates).

Competition assays with the unlabeled T2 inhibitor (Figure and Table S7) yielded consistent K i values across all monochromator and filter combinations (from 6.6 ± 1.6 to 14 ± 2.5 nM). Minimal variation in K i values with different tracers or detection settings indicated robust and reliable assay performance regardless of the detection configuration. These findings demonstrate both tracers’ remarkable versatility in NanoBRET assays with appropriate wavelength combinations. T2-BDP-589 performs optimally in this format, requiring lower probe concentrations for effective target engagement, enhanced signal specificity, reduced risk of inner-filter effects, and minimal probe-induced cytotoxicity. The effectiveness of T2-BDP-FL in NanoBRET highlights the potential for using a single tracer across multiple assay platforms, streamlining assay development and enabling direct comparison of biochemical and cellular data. The versatility and scalability of this system were further reinforced by successful NanoBRET target engagement assays in HeLa cells with BTK-NanoLuc, as detailed in Figures S3 and S4 and Tables S8 and S9.

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NanoBRET competition assays for RIPK1 target engagement. Normalized BRET ratio curves demonstrate displacement of T2-BODIPY-FL and T2-BODIPY-589 tracers by increasing concentrations of unlabeled T2 inhibitor in NanoLuc-RIPK1 expressing HEK293T cells, across various NanoBRET detection parameters. Data are presented as the mean ± SD (n = 4 technical replicates).

Discussion

Our evaluation of T2-BDP-FL and T2-BDP-589 across TR-FRET and NanoBRET platforms reveals unexpected tracer versatility, challenging the conventional approach of developing platform-specific probes.

Both assays determined the binding affinities of a T2 inhibitor against RIPK1, a therapeutic target. In TR-FRET assays, T2-BDP-FL exhibited a K d of 608 nM (Z′ = 0.57 and signal window = 6.0) with the 520 ± 10/490 ± 10 nm filter pair. T2-BDP-589 demonstrated a slightly lower K d of 443 nM (Z′ = 0.53 and signal window = 7.8) with the 640 ± 10/490 ± 10 nm filter pair. This suggests that T2-BDP-589 may have a higher affinity under optimized TR-FRET conditions. However, K d variations may stem from filter settings capturing distinct emission regions, affecting signal intensity and binding metrics rather than true affinity differences. Supporting this, competition assays with an unlabeled T2 inhibitor yielded different K i values (18 ± 0.70 nM for T2-BDP-FL and 54 ± 5.9 nM for T2-BDP-589), with discrepancies likely arising from differences in detection parameters.

In NanoBRET assays, both tracers demonstrated strong binding to RIPK1, with T2-BDP-589 outperforming T2-BDP-FL, achieving the highest Z′ factor (0.80) and signal window (15) using the 610 nm long-pass filter and 460 ± 30 nm monochromator setting. This improved performance is attributed to greater spectral separation between NanoLuc and T2-BDP-589, reducing inner-filter effects and signal bleed-through, ensuring more reliable data. Surprisingly, T2-BDP-FL also showed effective NanoBRET compatibility, particularly with the 520 ± 30 and 460 ± 30 nm monochromator setting (Z′ = 0.72 and signal window = 14), though its performance declined at longer wavelengths. Notably, similar K i values (6.6–14 nM) across multiple configurations confirmed both tracers’ suitability for quantitative target engagement studies.

The cross-platform utility of these tracers was additionally validated across different kinase targets, including BTK, in both biochemical and cellular contexts (Figures S1 and S2 and Tables S4 and S5 for TR-FRET and Figures S3 and S4 and Tables S8 and S9 for NanoBRET). Given these consistent results, we next considered the intrinsic properties of each tracer to guide optimal application. T2-BDP-FL offers a higher quantum yield and established performance in TR-FRET assays, making it excellent for biochemical screening. However, its shorter emission wavelength increases the susceptibility to autofluorescent interference. In contrast, T2-BDP-589, red-shifted spectral properties, provide superior resistance to such interference, especially valuable when screening compound libraries or working in complex biological matrices. Its longer emission wavelength (589 nm) enhances separation from both terbium and NanoLuc emissions, reducing signal bleed-through and improving signal-to-background ratios.

We recommend T2-BDP-589 as the preferred tracer for integrated assay development across both platforms. Its TR-FRET functionality, excellent performance in NanoBRET, and red-shifted spectral properties that minimize autofluorescent interference make it a more versatile choice for comprehensive target engagement studies. However, for laboratories focused exclusively on TR-FRET screening without cellular validation, T2-BDP-FL remains an excellent option.

The cross-platform compatibility of these fluorescent tracers represents a significant advancement for assay development in drug discovery. This approach challenges the conventional notion that different assay platforms require distinct tracers, potentially streamlining probe development and reducing costs and complexity. By using identical tracers across biochemical and cellular formats, researchers can make more reliable comparisons between in vitro and cellular data without introducing tracer-specific variables that might otherwise confound interpretation. The principles established here have broad applicability beyond kinases, potentially extending to diverse target classes, including membrane receptors, nuclear receptors, and protein–protein interactions, where consistent target engagement measurements across platforms provide valuable insights. Furthermore, this methodology lays the foundation for developing multiplexed assays capable of simultaneously monitoring interactions with multiple targets, offering a more comprehensive view of the compound selectivity and engagement profiles.

In conclusion, our evaluation demonstrates that carefully designed fluorescent tracers can effectively function across both biochemical and cellular assay platforms. By selecting tracers with appropriate spectral properties, particularly those with red-shifted emission profiles like T2-BDP-589, researchers can streamline assay development, reduce variability, and obtain more consistent target engagement data across experimental systems.

Supplementary Material

ml5c00171_si_001.pdf (1.5MB, pdf)

Acknowledgments

The research was supported in part by the National Institute of Health (R01-CA268518 and R01-CA250503 to Jin Wang), the Cancer Prevention & Research Institute of Texas (CPRIT, RP220480 to Jin Wang), the Center for NextGen Therapeutics at BCM, and the Michael E. DeBakey, M.D., Professorship in Pharmacology to Jin Wang. The authors also acknowledge Dr. Mazitschek and BMG Labtech’s advice and assistance with the customized filters.

Glossary

Abbreviations Used

TR-FRET

time-resolved fluorescence resonance energy transfer

NanoBRET

nanoluciferase bioluminescence resonance energy transfer

NanoLucⓇ

NanoLuciferase

RIPK1

receptor-interacting protein kinase 1

HEK293T

human embryonic kidney 293T cells

K d

equilibrium dissociation constant

K i

inhibition constant

Z′

Z-prime factor

LP

long-pass filter

BODIPY

dipyrrometheneboron difluoride

Tb

terbium

FL

fluorescein

NCT

nonchloro TOM

IC50

half-maximal inhibitory concentration

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsmedchemlett.5c00171.

  • Additional references, comprehensive experimental methods detailing chemical synthesis and biological assays, as well as supplementary figures and tables (PDF)

Erika Y. Monroy and Jin Wang designed experiments. Erika Y. Monroy performed experiments. Xin Yu and Dong Lu provided fluorescent tracers. Erika Y. Monroy, Xiaoli Qi, and Jin Wang wrote the manuscript.

The authors declare the following competing financial interest(s): Jin Wang is the co-founder of Chemical Biology Probes LLC. Jin Wang has stock ownership in CoRegen, Inc. and serves as a consultant for this company. Jin Wang and Xin Yu are the co-founders of Fortitude Biomedicines, Inc. and hold equity interest in this company.

References

  1. Nada H., Choi Y., Kim S., Jeong K. S., Meanwell N. A., Lee K.. New Insights into Protein-Protein Interaction Modulators in Drug Discovery and Therapeutic Advance. Signal Transduct. Target. Ther. 2024;9(1):1–32. doi: 10.1038/s41392-024-02036-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Murali S., Rustandi R. R., Zheng X., Payne A., Shang L.. Applications of Surface Plasmon Resonance and Biolayer Interferometry for Virus-Ligand Binding. Viruses. 2022;14(4):717. doi: 10.3390/v14040717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Qi C., Mankinen O., Telkki V.-V., Hilty C.. Measuring Protein-Ligand Binding by Hyperpolarized Ultrafast NMR. J. Am. Chem. Soc. 2024;146(8):5063–5066. doi: 10.1021/jacs.3c14359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Jarmoskaite I., AlSadhan I., Vaidyanathan P. P., Herschlag D.. How to Measure and Evaluate Binding Affinities. eLife. 2020;9:e57264. doi: 10.7554/eLife.57264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Wegner K. D., Jin Z., Lindén S., Jennings T. L., Hildebrandt N.. Quantum-Dot-Based Förster Resonance Energy Transfer Immunoassay for Sensitive Clinical Diagnostics of Low-Volume Serum Samples. ACS Nano. 2013;7(8):7411–7419. doi: 10.1021/nn403253y. [DOI] [PubMed] [Google Scholar]
  6. Blay V., Tolani B., Ho S. P., Arkin M. R.. High-Throughput Screening: Today’s Biochemical and Cell-Based Approaches. Drug Discovery Today. 2020;25(10):1807–1821. doi: 10.1016/j.drudis.2020.07.024. [DOI] [PubMed] [Google Scholar]
  7. Busby S. A., Carbonneau S., Concannon J., Dumelin C. E., Lee Y., Numao S., Renaud N., Smith T. M., Auld D. S.. Advancements in Assay Technologies and Strategies to Enable Drug Discovery. ACS Chem. Biol. 2020;15(10):2636–2648. doi: 10.1021/acschembio.0c00495. [DOI] [PubMed] [Google Scholar]
  8. Yu X., Lin H., Li F., Wang J., Lu D.. Development of Biochemical and Cellular Probes to Study RIPK1 Target Engagement. ACS Med. Chem. Lett. 2024;15(6):906–916. doi: 10.1021/acsmedchemlett.4c00104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gorshkov K., Morales Vasquez D., Chiem K., Ye C., Nguyen Tran B., Carlos de la Torre J., Moran T., Chen C. Z., Martinez-Sobrido L., Zheng W.. SARS-CoV-2 Nucleocapsid Protein TR-FRET Assay Amenable to High Throughput Screening. ACS Pharmacol. Transl. Sci. 2022;5(1):8–19. doi: 10.1021/acsptsci.1c00182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Larson J. E., Hardy P. B., Schomburg N. K., Wang X., Kireev D., Rossman K. L., Pearce K. H.. Development of a High-Throughput TR-FRET Screening Assay for a Fast-Cycling KRAS Mutant. SLAS Discovery. 2023;28(1):39–47. doi: 10.1016/j.slasd.2022.12.001. [DOI] [PubMed] [Google Scholar]
  11. Abdel-Rahman S. A., Zhang L., Gabr M. T.. Development of a High-Throughput TR-FRET Screening Assay for LAG-3/FGL1 Interaction. SLAS Discovery. 2023;28(4):188–192. doi: 10.1016/j.slasd.2023.04.003. [DOI] [PubMed] [Google Scholar]
  12. Lin H., Riching K., Lai M. P., Lu D., Cheng R., Qi X., Wang J.. Lysineless HiBiT and NanoLuc Tagging Systems as Alternative Tools for Monitoring Targeted Protein Degradation. ACS Med. Chem. Lett. 2024;15(8):1367–1375. doi: 10.1021/acsmedchemlett.4c00271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Zhang J.-H., Chung T. D. Y., Oldenburg K. R.. Confirmation of Primary Active Substances from High Throughput Screening of Chemical and Biological Populations: A Statistical Approach and Practical Considerations. J. Comb. Chem. 2000;2(3):258–265. doi: 10.1021/cc9900706. [DOI] [PubMed] [Google Scholar]
  14. Cho U., Chen J. K.. Lanthanide-Based Optical Probes of Biological Systems. Cell Chem. Biol. 2020;27(8):921–936. doi: 10.1016/j.chembiol.2020.07.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Degorce F.. HTRF: A Technology Tailored for Drug DiscoveryA Review of Theoretical Aspects and Recent Applications. Curr. Chem. Genomics. 2009;3(1):22–32. doi: 10.2174/1875397300903010022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Algar W. R., Hildebrandt N., Vogel S. S., Medintz I. L.. FRET as a Biomolecular Research Tool  Understanding Its Potential While Avoiding Pitfalls. Nat. Methods. 2019;16(9):815–829. doi: 10.1038/s41592-019-0530-8. [DOI] [PubMed] [Google Scholar]
  17. Machleidt T., Robers M. B., Hermanson S. B., Dudek J. M., Bi K.. TR-FRET Cellular Assays for Interrogating Posttranslational Modifications of Histone H3. J. Biomol. Screen. 2011;16(10):1236–1246. doi: 10.1177/1087057111422943. [DOI] [PubMed] [Google Scholar]
  18. Wesley N. A., Skrajna A., Simmons H. C., Budziszewski G. R., Azzam D. N., Cesmat A. P., McGinty R. K.. Time Resolved-Fluorescence Resonance Energy Transfer Platform for Quantitative Nucleosome Binding and Footprinting. Protein Sci. 2022;31(6):e4339. doi: 10.1002/pro.4339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Greenway H., Wang J.. Evaluation of High-Affinity Monoclonal Antibodies and Antibody-Drug Conjugates by Homogenous Time-Resolved FRET. ACS Med. Chem. Lett. 2024;15(9):1598–1605. doi: 10.1021/acsmedchemlett.4c00317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lin W., Li Y., Min J., Liu J., Yang L., Lee R. E., Chen T.. Development of BODIPY FL Thalidomide As a High-Affinity Fluorescent Probe for Cereblon in a Time-Resolved Fluorescence Resonance Energy Transfer Assay. Bioconjugate Chem. 2020;31(11):2564–2575. doi: 10.1021/acs.bioconjchem.0c00507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Xu Y., Piston D. W., Johnson C. H.. A Bioluminescence Resonance Energy Transfer (BRET) System: Application to Interacting Circadian Clock Proteins. Proc. Natl. Acad. Sci. U.S.A. 1999;96(1):151–156. doi: 10.1073/pnas.96.1.151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Machleidt T., Woodroofe C. C., Schwinn M. K., Méndez J., Robers M. B., Zimmerman K., Otto P., Daniels D. L., Kirkland T. A., Wood K. V.. NanoBRETA Novel BRET Platform for the Analysis of Protein-Protein Interactions. ACS Chem. Biol. 2015;10(8):1797–1804. doi: 10.1021/acschembio.5b00143. [DOI] [PubMed] [Google Scholar]
  23. Hall M. P., Unch J., Binkowski B. F., Valley M. P., Butler B. L., Wood M. G., Otto P., Zimmerman K., Vidugiris G., Machleidt T., Robers M. B., Benink H. A., Eggers C. T., Slater M. R., Meisenheimer P. L., Klaubert D. H., Fan F., Encell L. P., Wood K. V.. Engineered Luciferase Reporter from a Deep Sea Shrimp Utilizing a Novel Imidazopyrazinone Substrate. ACS Chem. Biol. 2012;7(11):1848–1857. doi: 10.1021/cb3002478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Liu Y., Song G., Shao X.-X., Liu Y.-L., Guo Z.-Y.. Quantitative Measurement of Cell Membrane Receptor Internalization by the Nanoluciferase Reporter: Using the G Protein-Coupled Receptor RXFP3 as a Model. Biochim. Biophys. Acta BBA - Biomembr. 2015;1848(2):688–694. doi: 10.1016/j.bbamem.2014.11.026. [DOI] [PubMed] [Google Scholar]
  25. Lohse M. J., Nuber S., Hoffmann C.. Fluorescence/Bioluminescence Resonance Energy Transfer Techniques to Study G-Protein-Coupled Receptor Activation and Signaling. Pharmacol. Rev. 2012;64(2):299–336. doi: 10.1124/pr.110.004309. [DOI] [PubMed] [Google Scholar]
  26. Mahan, S. D. ; Riching, K. M. ; Urh, M. ; Daniels, D. L. . Kinetic Detection of E3:PROTAC:Target Ternary Complexes Using NanoBRETNanoBRET Technology in Live Cells. In Targeted Protein Degradation: Methods and Protocols; Cacace, A. M. , Hickey, C. M. , Békés, M. , Eds.; Springer: New York, 2021; Methods in Molecular Biology, Vol. 2365, pp 151–171, 10.1007/978-1-0716-1665-9_8. [DOI] [PubMed] [Google Scholar]
  27. Robers M. B., Dart M. L., Woodroofe C. C., Zimprich C. A., Kirkland T. A., Machleidt T., Kupcho K. R., Levin S., Hartnett J. R., Zimmerman K., Niles A. L., Ohana R. F., Daniels D. L., Slater M., Wood M. G., Cong M., Cheng Y.-Q., Wood K. V.. Target Engagement and Drug Residence Time Can Be Observed in Living Cells with BRET. Nat. Commun. 2015;6(1):10091. doi: 10.1038/ncomms10091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Robers M. B., Friedman-Ohana R., Huber K. V. M., Kilpatrick L., Vasta J. D., Berger B.-T., Chaudhry C., Hill S., Müller S., Knapp S., Wood K. V.. Quantifying Target Occupancy of Small Molecules Within Living Cells. Annu. Rev. Biochem. 2020;89:557–581. doi: 10.1146/annurev-biochem-011420-092302. [DOI] [PubMed] [Google Scholar]
  29. Yu, X. ; Wang, J. . Quantitative Measurement of PROTAC Intracellular Accumulation. In Targeted Protein Degradation; Burslem, G. L. , Ed.; Academic Press: Cambridge, MA, 2023; Methods in Enzymology, Vol. 681, Chapter 9, pp 189–214, 10.1016/bs.mie.2022.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Dopfer J., Vasta J. D., Müller S., Knapp S., Robers M. B., Schwalm M. P.. tracerDB: A Crowdsourced Fluorescent Tracer Database for Target Engagement Analysis. Nat. Commun. 2024;15(1):5646. doi: 10.1038/s41467-024-49896-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Cho U., Riordan D. P., Ciepla P., Kocherlakota K. S., Chen J. K., Harbury P. B.. Ultrasensitive Optical Imaging with Lanthanide Lumiphores. Nat. Chem. Biol. 2018;14(1):15–21. doi: 10.1038/nchembio.2513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Bünzli J.-C. G.. Lanthanide Luminescence for Biomedical Analyses and Imaging. Chem. Rev. 2010;110(5):2729–2755. doi: 10.1021/cr900362e. [DOI] [PubMed] [Google Scholar]
  33. Payne N. C., Kalyakina A. S., Singh K., Tye M. A., Mazitschek R.. Bright and Stable Luminescent Probes for Target Engagement Profiling in Live Cells. Nat. Chem. Biol. 2021;17(11):1168–1177. doi: 10.1038/s41589-021-00877-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Yu X., Lu D., Qi X., Paudel R. R., Lin H., Holloman B. L., Jin F., Xu L., Ding L., Peng W., Wang M. C., Chen X., Wang J.. Development of a RIPK1 Degrader to Enhance Antitumor Immunity. Nat. Commun. 2024;15(1):10683. doi: 10.1038/s41467-024-55006-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Vasta J. D., Corona C. R., Wilkinson J., Zimprich C. A., Hartnett J. R., Ingold M. R., Zimmerman K., Machleidt T., Kirkland T. A., Huwiler K. G., Ohana R. F., Slater M., Otto P., Cong M., Wells C. I., Berger B.-T., Hanke T., Glas C., Ding K., Drewry D. H., Huber K. V. M., Willson T. M., Knapp S., Müller S., Meisenheimer P. L., Fan F., Wood K. V., Robers M. B.. Quantitative, Wide-Spectrum Kinase Profiling in Live Cells for Assessing the Effect of Cellular ATP on Target Engagement. Cell Chem. Biol. 2018;25(2):206–214. doi: 10.1016/j.chembiol.2017.10.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. PROMEGA Corporation . NanoBRETTM TE 590 Dyes. PROMEGA Corporation: Madison, WI, 2024; https://www.promega.com/-/media/files/resources/protocols/technical-manuals/500/nanobret-te-590-dyes-protocol-tm697.pdf?rev=d99161c1112742cd9fb67d889fcffd95&sc_lang=en (accessed April 15, 2024). [Google Scholar]
  37. Lindén S., Singh M. K., Wegner K. D., Regairaz M., Dautry F., Treussart F., Hildebrandt N.. Terbium-Based Time-Gated Förster Resonance Energy Transfer Imaging for Evaluating Protein–Protein Interactions on Cell Membranes. Dalton Trans. 2015;44(11):4994–5003. doi: 10.1039/C4DT02884H. [DOI] [PubMed] [Google Scholar]
  38. AAT Bioquest, Inc. Quest GraphTM Spectrum [BODIPY FL]; AAT Bioquest, Inc.: Pleasanton, CA, 2025; https://www.aatbio.com/fluorescence-excitation-emission-spectrum-graph-viewer/bodipy_fl (accessed March 10, 2025). [Google Scholar]
  39. AAT Bioquest, Inc. Quest GraphTM Spectrum [BODIPYTM 576/589]; AAT Bioquest, Inc.: Pleasanton, CA, 2025; https://www.aatbio.com/fluorescence-excitation-emission-spectrum-graph-viewer/bodipy_576_589 (accessed March 10, 2025). [Google Scholar]
  40. Dale N. C., Johnstone E. K. M., White C. W., Pfleger K. D.. G. NanoBRET: The Bright Future of Proximity-Based Assays. Front. Bioeng. Biotechnol. 2019;7:56. doi: 10.3389/fbioe.2019.00056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Yung-Chi C., Prusoff W. H.. Relationship between the Inhibition Constant (KI) and the Concentration of Inhibitor Which Causes 50 per Cent Inhibition (I 50) of an Enzymatic Reaction. Biochem. Pharmacol. 1973;22(23):3099–3108. doi: 10.1016/0006-2952(73)90196-2. [DOI] [PubMed] [Google Scholar]
  42. Kurata S., Kanagawa T., Yamada K., Torimura M., Yokomaku T., Kamagata Y., Kurane R.. Fluorescent Quenching-Based Quantitative Detection of Specific DNA/RNA Using a BODIPY® FL-Labeled Probe or Primer. Nucleic Acids Res. 2001;29(6):e34. doi: 10.1093/nar/29.6.e34. [DOI] [PMC free article] [PubMed] [Google Scholar]

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