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Article

Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes †

by
Alaa Maatouk
,
Thibaud Rossel
and
Bruno Therrien
*
Institute of Chemistry, University of Neuchatel, Ave. de Bellevaux 51, CH-2000 Neuchatel, Switzerland
*
Author to whom correspondence should be addressed.
†
This article is dedicated to Reinhard Neier on the occasion of his 75th birthday.
Inorganics 2025, 13(1), 1; https://doi.org/10.3390/inorganics13010001
Submission received: 4 December 2024 / Revised: 17 December 2024 / Accepted: 18 December 2024 / Published: 24 December 2024
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Abstract

:
A water-soluble arene ruthenium metalla-rectangle (MR1) functionalized with boronic acid groups was used to host various fluorescent dyes (fluorescein, eosin Y, and erythrosin B). These simple host–guest systems partially quench the natural fluorescence of the dyes, which can be regained in the presence of saccharides, phosphorylated molecules, and other analytes. The intensity of the regained fluorescence is directly linked to the nature of the analyte, and it shows some dose–response relationships with saccharides and phosphorylated molecules that are not compatible with a displacement assay, thus suggesting an allosteric mechanism. Interestingly, when fluorescein is trapped by the metalla-rectangle in the presence of D-fructose, half of the maximum fluorescence intensity is recovered at a fructose concentration of 17.2 ± 4.7 μM, while, for D-glucose, a concentration of 50.6 ± 2.5 μM is required for the same effect. Indeed, all combinations of analyte–host–dye (seven analytes, one host, three dyes) show a unique dose–response relationship in water at pH 8.0. However, in the presence of naphthalene and pyrene, fluorescein⸦MR1 shows a different behavior, acting as an indicator displacement assay with the full recovery of fluorescence. All data were analyzed by unsupervised machine learning technologies (PCA and cluster analysis), suggesting that such systems with multiple analyte–response behaviors are offering new perspectives for the development of highly sensitive, easily tunable, water-soluble, fluorescent-based sensing arrays for biomolecules and other analytes.

Graphical Abstract

1. Introduction

Nature provides the most advanced biological engineering technology, thanks to Darwinian evolution over millions of years [1]. Proteins are certainly one of the best engineered machineries created by nature, showing multiple functions and long-distance regulation mechanisms. The ability for proteins to change conformation and to trigger different responses to stimuli has been coined “allostery” [2,3]. Therefore, in an attempt to mimic nature, chemists have been trying to develop allosteric systems, in which an event on one part of the molecule generates a second event elsewhere [4,5], taking notable advantages on second coordination sphere interactions [6,7,8]. Such cascades of events are particularly attractive for sensing technology, where selectivity and sensitivity are crucial for discriminating between analytes [9,10,11]. However, to succeed, variable responses have to be produced by the sensor in the presence of analytes, to be able to generate a versatile sensing array [12,13,14,15]. With the emergence of rational design coupled with high-throughput screening and combinatorial chemistry [16,17], we can now take advantage of artificial intelligence and robotics in chemistry to develop such sensing arrays [18]. In this context, we can now envisage the development of sensors based on allosteric mechanisms, thus expanding the chemical universe of sensors [19,20].
Coordination-driven self-assembly (CDSA) is particularly suitable for developing sensors [21,22,23,24,25,26]. Most CDSAs possess a cavity where a guest compound can sit, offering a primary binding site [27,28,29], while functionalization at the periphery of the CDSA can generate additional binding sites for either analytes or fluorescent probes [30,31,32,33,34]. Accordingly, the interaction of analytes or probes with CDSA will modify their conformational structure and physical property, thus potentially triggering chemical responses [35]. Moreover, the attractiveness of most CDSA synthetic strategies resides in their ability to interchange building blocks, thus facilitating a modulating approach with combinatorial aptitude [36,37]. This is particularly true with arene ruthenium assemblies, which are commonly built from three components [38], the arene ruthenium units, spacers (tetradentate bridging ligands), and linkers (bis-, tris-, tetra-pyridyl ligands), thus offering numerous possibilities to add functional groups and to modify the solubility, shape, and size of the assembly [39,40,41].
In sensing, several key challenges are still opened, like the specific detection of saccharides, dioxins, quinones, or phosphates, which are all highly difficult analytes to discriminate from topological competitors [42,43,44]. When dealing with saccharides, most sensors exploit the unique affinity of boronic acid for diols to generate boronate esters [45]. Since the first publication of a boronic acid-based sensor for saccharides in the 1990s [46], the number of publications in the field has grown tremendously. In parallel, the sophistication of the sensors has increased drastically, moving from one to two boronic acid functions per sugars to increase the binding affinity [47], to sensors with a nitrogen atom next to the boronic acid active site [48,49,50], to sensors incorporating fluorescent probes for rapid visual detection [51,52,53,54], to sensors exploiting indicator displacement assay mechanisms [55,56,57], to sensors with redox active functional groups [58,59,60], or to hybrid materials incorporating multiple boronic acid functions [61,62,63,64,65]. Therefore, to develop a new family of sensors, we have used a different scaffold: A water-soluble coordination-driven arene ruthenium assembly functionalized with boronic acids. Then, we have studied its ability to sense various analytes, from saccharides to phosphorylated molecules as well as planar aromatics.

2. Results

The synthesis of the boronic acid functionalized metalla-rectangle [{Ru(p-cymene)}4(bpe)2(babo)2]4+ (MR1) is straightforward (Scheme 1). It is performed in methanol at reflux, by mixing the dinuclear complex [{Ru(p-cymene)}2(babo)Cl2] (babo-H2 = N,N′-bis{4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)benzyl}oxalamide) with 1,2-bis(4-pyridyl)ethylene (bpe) in the presence of silver triflate (see the SI for full description and characterization). The tetracationic complex is isolated as its triflate salt in excellent yield (94%). The stereogenic nature of the ruthenium atoms reduces the symmetry, thus complicating the 1H NMR spectrum (Figure S12). Nevertheless, the corresponding DOSY (diffusion-ordered spectroscopy) shows a single compound in solution (Figure S14), supporting the formation of MR1. Moreover, multiple peaks in the ESI-MS spectrum (Figure S16) confirm the formation of the assembly, especially the one at m/z = 720.15, which correspond to [MR1 + CF3SO3]3+ (Figure S26a) and at m/z = 830.58 for the host–guest fluorescein⸦MR1 system [MR1 + fluorescein + CF3SO3]3+ (Figure S26b). Such multiple-charged peaks of intact arene ruthenium metalla-assembly plus counter anions with or without guests are commonly observed with arene ruthenium assemblies [66,67].
Then, three fluorescent dyes (fluorescein, eosin Y, and erythrosin B) were added to MR1 to form the corresponding host–guest systems, fluorophore⸦MR1. Interestingly, the quenching of the fluorescence for a 1:1 host–guest ratio was not total in water at pH 8.0 (HEPES buffer), and the encapsulation process of the dyes have shown different dynamic behaviors. In the case of fluorescein, heating at 35 °C overnight was required to reach equilibrium, and the fluorescence was quenched by 34% (Tables S1 and S2). On the other hand, for eosin Y and erythrosin B, an equilibrium was reached in only two hours at room temperature, with quenching values of 14% and 17%, respectively. The quenching behavior for a 1:1 MR1: fluorophore ratio has raised questions on the nature of the host–guest interactions and the location of the fluorophore in MR1. Therefore, further studies involving MR1 and fluorophores were conducted to better understand the interactions that take place in water (pH 8.0) between the fluorophore and MR1.
We have first evaluated the maximum quenching capacity of MR1 with fluorophores. A gradual addition of fluorescein to an aqueous solution containing MR1 showed that maximum quenching (≈ 50%) is reached when 2 equivalents of fluorescein are added (Figure S18). Similar results were obtained with eosin Y and erythrosin B, where maximum quenching was clearly obtained with two equivalents of fluorophore (Figure S18). Therefore, one can assume that each face of MR1, and accordingly up to two boronic acid functions, interacts with a fluorophore, thus forming overall (fluorophore)2⸦MR1 aggregates under these conditions. In these aggregates, the boronic acid functions are probably acting as Lewis acid, thus forming at pH 8.0 new B−-O bonds with the anionic fluorophore [68]. Indeed, to corroborate this assumption, we also looked at the quenching capacity of MR1 at pH 7.5 and 9.5, respectively. In both cases, no significant quenching effect was observed, thus suggesting that at pH 8.0 the boronic acid groups interact preferentially with the anionic form of the fluorophore instead of water (H2O at pH 7.5 and OH− at pH 9.5), a well-known phenomenon in boronic acid–saccharide chemistry [69]. Therefore, to keep one side of the assembly and boronic acid functions free for interacting with analytes, we have focused our attention to 1:1 fluorophore⸦MR1 systems (Figure 1), working in aqueous solution at pH 8.0 in the presence of HEPES buffer.
We have screened the three fluorophores⸦MR1 systems with multiple analytes (D-fructose, D-glucose, D-galactose, D-ribose, sodium triphosphate, D-ribose-5-phosphate, adenosine triphosphate) and determined the binding affinity by fluorescence spectroscopy (Figure 2). The fluorophore⸦MR1 sensors were dissolved in HEPES (10 mM solution, pH = 8.0) to a final concentration of 25 μM, and 1 to 10 equivalents of analytes were incrementally added to the mixture (see SI for more details). The initial fluorescence of the fluorophore⸦MR1 sensor was systematically subtracted, and only the regained fluorescence was used to plot the sensing ability of fluorophore⸦MR1 and to determine the effective concentration (EC50) of the sensors (Table 1).
As presented in Figure 2, all fluorophore⸦MR1 sensors show a different dose–response curve for each analyte, as well as substantial differences in efficacy. This is better illustrated when looking at the EC50 values and the corresponding heat map chart (Table 1). The weakest response was observed between D-ribose-5-phosphate and eosin Y⸦MR1 (110.5 ± 7.8 μM), followed by D-fructose with the erythrosin B⸦MR1 system (79.7 ± 6.7 μM). On the other hand, the lowest EC50 values, which correspond to higher efficacy, were observed for D-fructose with fluorescein⸦MR1 (17.2 ± 4.7 μM) and D-glucose with erythrosin B⸦MR1 (30.6 ± 1.4 μM). In the case of D-glucose and D-galactose with eosin Y⸦MR1, no clear inflexion points were observed (Figure 2b), and accordingly, no EC50 values determined. This behavior suggests that the interaction between eosin Y and MR1 is negligibly affected by the presence of these two analytes. Such behavioral differences between sensors and analytes are clearly the premises for developing fluorescent-based sensing arrays.
Moreover, from Figure 2a, we can observe a clear discrepancy between the shape of the dose–response curves; the saccharides (dotted lines) are rapidly flattening after the addition of a second equivalent of analyte (50 μM), while for the phosphorylated molecules, 10 equivalents of analytes (solid lines) are just enough to reach a plateau. This can be linked to the binding mode of these analytes with the fluorescein⸦MR1 system. For sugars, it is well-known that boronic acids form up to two bonds with diols to generate stable boronate esters [70], while with phosphates, a single bond is formed with higher lability [71,72,73,74]. Therefore, as suggested by the shape of these dose–response curves, different analytes prompt different responses from the fluorescein⸦MR1 sensor, most likely by conformational changes. The sugars seem to interact strongly with the free boronic acid functions of MR1, thus having a variable impact on the partial quenching of the fluorescence, which remains stable after two equivalents of analytes. In the case of phosphorylated molecules, interactions appear to be weaker, as more equivalents of analytes are needed to reach an equilibrium, but they also show a higher impact on the quenching process. Therefore, we can conclude that different dynamic processes are taking place, depending on the nature of the analyte (see Figure 7).
To confirm the importance of the boronic acid functions, a known cationic metalla-rectangle with four hydroxy ethyl groups was prepared [67] and tested with fluorescein as fluorophore, in the presence or absence of the same analytes (Figure 3). This metalla-rectangle (MR2), which possesses a cavity similar to that of MR1, interacts with fluorescein, showing partial quenching of the fluorescence (≈12%). However, maximum quenching was reached after the addition of only one equivalent of the fluorophore (Figure S19), suggesting that fluorescein sits in the cavity of MR2 and interacts mainly by π-stacking and electrostatic interactions, and not with the hydroxyl groups at the periphery of the rectangle. Accordingly, upon the gradual addition of analytes, the quenching of fluorescence remains the same, having no effect on the host–guest interactions and the photophysical property of the fluorescein⸦MR2 system (Figure 3). Overall, these results confirm that another mode of interaction between the fluorophore and the non-boronic acid functionalized metalla-rectangle MR2 is in action here, as compared to the fluorescein⸦MR1 system (see Figure 7). These results support our design, in which boronic acid functional groups are crucial for sensing saccharides and to some extent phosphorylated molecules, and that such analytes can disrupt the quenching property of the fluorophore⸦MR1 systems by allosteric mechanisms.
Then, we have focused our attention to other analytes, especially those who are not expected to interact with boronic acid functions, such as planar aromatic molecules. In the past, pyrene and naphthalene have shown strong interactions with the hydrophobic cavity of arene ruthenium assemblies, showing excellent binding affinity [75]. Titrations of fluorescein⸦MR1 with naphthalene and pyrene (Figure 4) show another dose–response than those observed for saccharides and phosphorylated molecules, consistent with displacement assays (see Figure 7), despite the poor solubility of planar aromatics in aqueous solutions (1% DMF). A DOSY experiment supports the displacement mechanism, which, upon the addition of five equivalents of naphthalene to a CD3OD solution of fluorescein⸦MR1, a new diffusion coefficient is observed, where fluorescein is no more attached to MR1 and is replaced by naphthalene (Figures S24 and S25). This result suggests that, upon encapsulation of pyrene or naphthalene in the hydrophobic cavity of MR1, the binding of fluorescein to the boronic acid functionalized MR1 assembly is weakened, and fluorescein is eventually released from the fluorescein⸦MR1 adduct. In addition, it shows that the solvent is another variable that can be used to modulate the sensing mechanisms.
Considering the facility to combine arene ruthenium metalla-rectangles (MR1, MR2) with fluorophores (fluorescein, eosin Y, erythrosin B) and the rapidity to measure the variation of fluorescence to evaluate the sensing ability of the host–guest systems with multiple analytes (D-fructose, D-glucose, D-galactose, D-ribose, sodium triphosphate, D-ribose-5-phosphate, adenosine triphosphate, naphthalene, pyrene), a considerable amount of data was acquired in a reasonable timeframe. Therefore, we have used online unsupervised machine learning technology to analyze and reduce the dimensionality of the data and to draw some conclusions [76]. In this context, three categories of data were identified from the main titration experiments: EC50, Hill slope, and fluorescence variation (ΔFmax). The analysis revealed that the three principal components accounted for over 85% of the total variance, indicating a significant reduction in complexity, while retaining most of the information. Specifically, the first component was heavily influenced by the EC50 values, suggesting that this metric possesses the main variability. The second and third principal components were predominantly associated with the Hill slope and ΔFmax, respectively (Figure 5). The insights gained from PCA analysis highlighted the critical role of EC50 in differentiating analytes, with Hill slope and ΔFmax providing additional nuances, being fully consistent with the visual analysis of the dose–response titrations (Figure 2, Table 1).
In addition, to better understand the structure and relationships within our dataset, we performed a clustering analysis using K-means clustering [77]. This method allows us to group similar data points, providing insights into the underlying patterns and potential subgroups within the data. The K-means algorithm was applied to the transformed data with three clusters, the number of clusters based on the elbow method [78]. The clustering was performed on the standardized values of the principal components, showing three distinct groups within the dataset. This clustering analysis is visualized using a 3D scatter plot (Figure 6). The following key observations are made for each cluster. Cluster 0 includes analytes that exhibit high EC50 values (low efficacy) and a variable range of ΔFmax values. In cluster 1, the analytes show moderate EC50 values and average Hill slopes, while ΔFmax are high. Finally, analytes in cluster 2 have low EC50, low Hill slopes, and variable ΔFmax. These clusters represent groups of analytes with similar recognition profiles, characterized by their EC50, Hill slope, and ΔFmax values. The visualization of these clusters provides valuable insights into the diversity and similarity among the analytes, helping to identify potential candidates for further investigations. Furthermore, this analysis highlights the importance of combining dimensionality reduction techniques, like PCA, with clustering algorithms to uncover meaningful patterns in complex datasets [79,80].

3. Materials and Methods

Commercially available solvents were purchased from Sigma-Aldrich® (St. Louis, MO, USA) and Fisher Scientific® (Waltham, MA, USA). All reagents and chemicals were purchased from Sigma Aldrich or Chemie Brunschwig AG® (Basel, Switzerland), while the fluorescent dyes (fluorescein, eosin Y, erythrosin B) were purchased from Acros Organics® (Geel, Belgium). All chemicals were used as received. Normal phase column chromatography was performed on a combiflash system with sfär 25 g cartridges purchased from Biotage® in Uppsala (Sweden). The (p-cymene)ruthenium chloride dimer [67], and the tetracationic metalla-rectangle MR2 [81], were prepared according to published methods (see the SI for full description and characterization of the compounds).
The 1H, 13C{1H} and DOSY NMR spectra were recorded on a Bruker Avance II 600 MHz at 20 °C in CDCl3 (7.2 ppm), CD3OD (3.3 ppm), DMSO-d6 (2.5 ppm), or (CD3)2CO (2.0 ppm) relative to residual solvents. The 11B{1H} NMR spectra were recorded on a Bruker Avance II 400 MHz spectrometers at 20 °C at the department of Chemistry, Biochemistry and Pharmacy in Bern (Switzerland). Multiplicities are reported in Hz as: s = singlet, d = doublet, t = triplet, q = quartet and m = multiplet. Electrospray ionization mass spectra were obtained in positive ion mode on an LTQ Orbitrap Elite instrument at the ISIC Mass Spectrometry Service (SSMI) in Lausanne (Switzerland).
Fluorescence measurements were carried out in black NUNC® 96 well plates using VICTOR® Nivo™ multimode plate reader. The instrument was stabilized to the required temperature (20 °C or 35 °C) for each measurement. All measurements were done at least in triplicate. Fluorescence spectra were recorded using an excitation wavelength of λex = 498 nm for fluorescein and λex = 525 nm for erythrosin B and eosin Y. The intensity of the fluorophore emission peak at λem = 515 nm for fluorescein and λem = 554 nm for erythrosine B and eosin Y was used to determine the binding constants for all analytes. Determination of binding constants for each analyte (displacement assay, dose-response assay) was carried out using Prism 5.0® version 10.2.1 for Mac (GraphPad Software).
Stock solutions of rectangles (0.1 M) were prepared in organic solvents, DMF for MR1 and CH3CN for MR2. Stock solutions of analytes and fluorophores were prepared in distilled water (0.1 M). Working solutions of rectangles, fluorophores and analytes were obtained by dilution of the 0.1 M stock solutions using 10 mM HEPES buffers at different pHs. The pH of the HEPES solutions was adjusted using solid NaOH and controlled with a FiveEasy METTLER TOLEDO® pH meter (see the SI for further details).
The association constants (Ka) were determined in black NUNC® 96 well plates and the fluorescence was monitored with a thermostatted VICTOR® Nivo™ multimode plate reader. The maximal fluorescence of the fluorophore⸦MR was measured after 2 h of reaction with λex = 498 nm and λem = 515 nm for fluorescein, λex = 525 nm and λem = 554 nm for erythrosin B and eosin Y. Determination of Ka were performed using a one-to-one binding model [81] (all graphics, titrations, and other experiments are described and presented in the SI).

4. Conclusions

Hybrid sensors, combining an arene ruthenium CDSA functionalized with boronic acid groups and fluorophore guests, have been synthesized and characterized. The fluorophore⸦MR1 adducts show interesting responses to analytes, acting like indicator displacement assays with planar aromatic molecules, while, with saccharides, an allosteric mechanism is taking place (Figure 7). With saccharides, the free boronic acids interact with diols forming two B‒O bonds, only disrupting the fluorophore–MR1 photophysical property without forcing dissociation of the fluorophore⸦MR1 adduct. On the other hand, with planar aromatic molecules, different interactions are involved, which free the fluorophore when sufficient amounts of analyte are added, thus acting as an indicator displacement assay. In the case of phosphorylated molecules, the system behaves differently, showing mixed responses.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/inorganics13010001/s1, Synthesis and characterization of all compounds, NMR data (1H, 13C{1H}, 11B{1H}, DOSY), ESI spectra, titrations, and association and dissociation constants.

Author Contributions

Conceptualization, B.T. and T.R.; methodology, A.M. and T.R.; validation, A.M., T.R. and B.T.; formal analysis, A.M. and T.R.; data curation, A.M. and T.R.; writing—original draft preparation, A.M., T.R. and B.T.; writing—review and editing, B.T.; visualization, A.M., T.R. and B.T.; supervision, T.R. and B.T.; project administration, B.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data supporting this article have been included as part of the Supplementary Materials [81,82].

Acknowledgments

We thank the University of Neuchatel for financial support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Inorganics 13 00001 sch001
Scheme 1. Synthesis of metalla-rectangle MR1, see the SI for more details.
Scheme 1. Synthesis of metalla-rectangle MR1, see the SI for more details.
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Figure 1. Fluorescent dyes and schematic representation of the 1:1 fluorophore⸦MR1 systems.
Figure 1. Fluorescent dyes and schematic representation of the 1:1 fluorophore⸦MR1 systems.
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Figure 2. Titrations of various analytes in the presence of (a) fluorescein⸦MR1 (25 μM conc., λex = 498 nm, λem = 515 nm), (b) eosin Y⸦MR1 (25 μM conc., λex = 498 nm, λem = 515 nm) and erythrosin B⸦MR1 (25 μM conc., λex = 498 nm, λem = 554 nm), in triplicate at pH = 8.0 (HEPES buffer).
Figure 2. Titrations of various analytes in the presence of (a) fluorescein⸦MR1 (25 μM conc., λex = 498 nm, λem = 515 nm), (b) eosin Y⸦MR1 (25 μM conc., λex = 498 nm, λem = 515 nm) and erythrosin B⸦MR1 (25 μM conc., λex = 498 nm, λem = 554 nm), in triplicate at pH = 8.0 (HEPES buffer).
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Figure 3. Structure of fluorescein⸦MR2 (top) and its response (25 μM conc., λex = 498 nm, λem = 515 nm) to analytes (>10 eq.) at pH 8.0 (HEPES buffer), at room temperature.
Figure 3. Structure of fluorescein⸦MR2 (top) and its response (25 μM conc., λex = 498 nm, λem = 515 nm) to analytes (>10 eq.) at pH 8.0 (HEPES buffer), at room temperature.
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Figure 4. Titrations of naphthalene and pyrene with fluorescein⸦MR1 at room temperature in HEPES buffer at pH 8.0 (1% DMF, 25 μM conc., λex = 498 nm, λem = 515 nm).
Figure 4. Titrations of naphthalene and pyrene with fluorescein⸦MR1 at room temperature in HEPES buffer at pH 8.0 (1% DMF, 25 μM conc., λex = 498 nm, λem = 515 nm).
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Figure 5. PCA analysis of the data with chatGPT-4o [76].
Figure 5. PCA analysis of the data with chatGPT-4o [76].
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Figure 6. K-means clustering from the PCA.
Figure 6. K-means clustering from the PCA.
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Figure 7. Schematic representation of the proposed mechanisms involved in the fluorophore⸦MR1 system (top, standard displacement assay; middle, hybrid displacement assay with boronic acid interactions; bottom, allosteric mechanism).
Figure 7. Schematic representation of the proposed mechanisms involved in the fluorophore⸦MR1 system (top, standard displacement assay; middle, hybrid displacement assay with boronic acid interactions; bottom, allosteric mechanism).
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Table 1. EC50 values (triplicates) and the corresponding heat map chart of the fluorophore⸦MR1 systems titrated with analytes.
Table 1. EC50 values (triplicates) and the corresponding heat map chart of the fluorophore⸦MR1 systems titrated with analytes.
ABCInorganics 13 00001 i001
analytefluorescein⸦MR1eosin Y⸦MR1erythrosin⸦MR1
1D-fructose17.2 ± 4.7 μM50.9 ± 4.4 μM79.7 ± 6.7 μM
2D-glucose50.6 ± 2.5 μMn.d.30.6 ± 1.4 μM
3D-galactose37.0 ± 4.1 μMn.d.61.4 ± 9.2 μM
4D-ribose24.3 ± 2.0 μM63.8 ± 4.8 μM64.6 ± 4.3 μM
5sodium triphosphate48.9 ± 3.8 μM61.4 ± 9.3 μM40.6 ± 2.2 μM
6D-ribose-5-phosphate24.3 ± 2.0 μM110.5 ± 7.8 μM50.1 ± 5.1 μM
7adenosine triphosphate53.9 ± 11.3 μM62.1 ± 3.7 μM66.4 ± 4.7 μM
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Maatouk, A.; Rossel, T.; Therrien, B. Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes. Inorganics 2025, 13, 1. https://doi.org/10.3390/inorganics13010001

AMA Style

Maatouk A, Rossel T, Therrien B. Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes. Inorganics. 2025; 13(1):1. https://doi.org/10.3390/inorganics13010001

Chicago/Turabian Style

Maatouk, Alaa, Thibaud Rossel, and Bruno Therrien. 2025. "Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes" Inorganics 13, no. 1: 1. https://doi.org/10.3390/inorganics13010001

APA Style

Maatouk, A., Rossel, T., & Therrien, B. (2025). Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes. Inorganics, 13(1), 1. https://doi.org/10.3390/inorganics13010001

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