# Study and optimization of the selectivity of an odorant binding protein-based bioelectronic nose

Marielle El Kazzy, Maxence Lalis, Charlotte Hurot, Jonathan Weerakkody, Raphael Mathey, Christine Saint-Pierre, Arnaud Buhot, Thierry Livache, Jérémie Topin, Lucie Moitrier, et al.

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Marielle El Kazzy, Maxence Lalis, Charlotte Hurot, Jonathan Weerakkody, Raphael Mathey, et al. Study and optimization of the selectivity of an odorant binding protein-based bioelectronic nose. Biosensors and Bioelectronics, 2025, 268, pp.116879. 10.1016/j.bios.2024.116879. hal-04774349

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# Study and Optimization of the Selectivity of an Odorant Binding Protein-based Bioelectronic Nose

Marielle El Kazzyª, Maxence Lalisᵇ, Charlotte Hurotª, Jonathan S. Weerakkodyª, Raphael Matheyª, Christine Saint-Pierreª, Arnaud Buhotª, Thierry Livacheª, Jérémie Topinᵇ, Lucie Moitrierᶜ, Christine Belloirᶜ, Loïc Briandᶜ, Yanxia Houª,*

## Abstract

Over the past two decades, the use of odorant-binding proteins (OBPs) for the development of biosensors and bioelectronic noses (bioeNs) aimed at detecting and analyzing volatile organic compounds (VOCs) has been the subject of considerable research. However, there is a lack of fundamental studies for better understanding the interaction between OBPs and VOCs in gas phase. In this work, we investigated the effect of two key factors, namely relative humidity (RH) level and immobilization technique, on the selectivity of two OBP-based biosensors in gas phase. Concerning the effect of RH, the results showed that our active OBP (wild-type rat OBP3) lost its selectivity at 0% RH but retained good selectivity at 30% and 50% RH. To better understand the effect of this parameter, the hydration mechanism of the OBP was studied both experimentally and through molecular dynamics simulations. The effect of a cysteine residue, genetically added to the N-terminus of OBPs to control their orientation after immobilization on the chip, was evaluated. A significant reduction in selectivity was observed in the absence of cysteine. As expected, the introduction of this amino acid enabled to control the orientation of OBPs, making their binding pocket more accessible to VOCs and favoring specific interactions. Furthermore, we demonstrated that combining OBP-based biosensors with different properties can improve the discrimination capability of our bioeN. Finally, the ability of our system to detect essential oil vapors was tested, providing preliminary evidence that our bioeN is capable of detecting VOCs in complex media.

## Keywords

Odorant-binding protein; surface plasmon resonance imaging; volatile organic compound; biosensor; electronic nose

<footnote>ª Université Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SyMMES, 17 Rue des Martyrs, 38000 Grenoble, France</footnote>

<footnote>ᵇ Université Côte d'Azur, Institut de Chimie de Nice UMR7272, CNRS, 28 Avenue Valrose, 06108 Nice, France</footnote>

<footnote>ᶜ Centre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne, F-21000 Dijon, France</footnote>

<footnote>*Corresponding author: Dr. Yanxia Hou, Address: IRIG-SyMMES, CEA-Grenoble, 17 Rue des Martyrs, 38854, Grenoble, France. Tel.: +33438789478. E-mail: yanxia.hou-broutin@cea.fr</footnote>
# 1. Introduction

Although imperceptible to the eye, volatile organic compounds (VOCs) are omnipresent in our environment and often carry considerable information. The detection and analysis of VOCs has garnered significant interest in a variety of fields, including environmental monitoring, public safety, the food and beverage industry, the cosmetics and fragrance industry, medical diagnostics, etc. Nowadays, the analysis of VOCs relies on two distinct types of methodology, one based on the human or canine sense of smell and the other on advanced analytical techniques (such as gas chromatography coupled with mass spectrometry or nuclear magnetic resonance). Although highly sensitive and effective, these techniques have a number of drawbacks: they are expensive, time-consuming and therefore unsuitable for large-scale use. This context has motivated the development of alternative technologies, notably electronic nose (eN) systems. eNs are biomimetic devices generally composed of an array of cross-reactive sensors combined with a pattern recognition system (Gardner and Bartlett, 1994). Over the past decades, eNs have demonstrated their significant potential and effectiveness as analytical tools for VOC analysis in many fields. Different types of sensor technologies, including chemiresistive (metal oxide semiconductors, conductive polymers), gravimetric or piezoelectric (quartz crystal microbalance, surface acoustic wave), optical and electrochemical have been used for the development of eN (Karakaya et al., 2020). In an effort to further mimic the performance of the biological nose and improve the sensitivity and selectivity of eN systems, one very promising strategy involves using biological materials derived from the olfactory system as sensing materials. In particular, the use of olfactory receptors (ORs) has been extensively studied (El Kazzy et al., 2020; Hou et al., 2007; Lagunas et al., 2022; Vidic et al., 2007). However, despite being very promising candidates, the use of ORs for the development of bioeN has proven difficult, mainly due to the fact that they are transmembrane proteins and their production and immobilization are very challenging (Wasilewski et al., 2018). Other proteins of the olfactory system, namely Odorant-Binding Proteins (OBPs), have also attracted considerable interest (El Kazzy et al., 2021, 2020; Hurot et al., 2020). The use of these proteins as sensing materials offers several advantages over ORs: they are easy to produce and purify, highly stable to temperature and pH variations, organic solvents and proteolytic degradation, and their binding properties can be genetically modified (Pelosi et al., 2018).

OBPs are small soluble proteins (~20 kDa) found in high concentrations in the nasal mucus of vertebrates and in the sensory lymph of insects (Pelosi and Knoll, 2022; Rihani et al., 2021). Vertebrate OBPs belong to the lipocalin family. Members of this family share a calyx-like architecture, consisting of a β-barrel housing the hydrophobic binding cavity and formed of eight antiparallel β-sheets and a short α-helical domain at the C-terminus. OBPs are able to bind different classes of VOCs with a micromolar range dissociation constant. Finally, although their physiological role is not fully elucidated, OBPs are thought to act as transporters of pheromones and/or odorants, facilitating their diffusion through aqueous mucus (Heydel et al., 2013).

Numerous studies have addressed the development of OBP-based biosensors in liquid phase, as this environment is more favorable to protein stability and activity. In this paper, we define a biosensor as a single-sensor system, while the bioelectronic nose is defined as a multi-sensor system. Different OBPs from vertebrates and insects have been used coupled with diverse transduction techniques including electrochemical impedance spectroscopy (Lu et al., 2015),
field-effect transistor (Kotlowski et al., 2018), localized surface plasmon resonance (Zhang et al., 2015), and micro-cantilevers (Manai et al., 2014). Our team has published a proof-of-concept study on the development of liquid-phase OBP-based bioeNs capable of detecting odorants with high sensitivity and selectivity (Hurot et al., 2019). Despite the commonly accepted notion that proteins need an aqueous environment to remain active, several studies (Cali and Persaud, 2020; Di Pietrantonio et al., 2013; Gao et al., 2020; Hou et al., 2005; Nardiello et al., 2022) have demonstrated that OBP-based biosensors are also efficient for the detection of odorants in a gaseous environment. Nevertheless, one of the studies reported that several measurement cycles in a dry environment could damage the proteins (Zhao et al., 2012). In addition, some studies indicated working under 22% (Cali and Persaud, 2020) and 40% (Bonnot et al., 2014) relative humidity (RH) levels. This suggests that the presence of water molecules remains crucial for the activity of OBP-based biosensors in gas phase. However, in the literature there is little fundamental study for better understanding the interaction between OBPs and VOCs under such conditions. To the best of our knowledge, no study examined the effect of humidity on the performance of these biosensors.

In this work, we investigated the effect of RH on the performance of OBP-based biosensors. To get a deeper insight into the influence of this parameter, the hydration mechanism of a model OBP (wild-type rat OBP3) was studied experimentally and by molecular dynamics (MD) simulations. Moreover, the effect of a cysteine residue, genetically added to the N-terminus of OBPs to control their orientation after immobilization on the chip, was also assessed. The combination of different OBP-based biosensors with different properties to improve the discrimination capability of our bioeN was explored. Finally, the ability of our system to detect essential oil vapors was tested. It should be noted that all studies reported in the literature have focused exclusively on the detection and discrimination of single VOC, but never VOC mixtures. Our work may pave the way for the use of OBP-based bioeN for the analysis of more complex samples.

## 2. Materials and methods

### 2.1. Materials

Sodium dihydrogen phosphate (NaH₂PO₄), glycerol and all volatile organic compounds: 2-isobutyl-3-methoxypyrazine (IBMP) (> 99%), 1-octanal (> 99%), and 1-hexanol (> 98%) were purchased from Sigma Aldrich. HPLC-MS grade water provided by Carlo Erba was used to prepare phosphate buffer solution. HPLC-MS grade water was used to avoid contamination of OBPs. Four different OBPs were used in this work: wild-type rat OBP3 (rOBP3-w), a mutant form of this protein that we called rOBP3-c, wild-type porcine OBP1 (pOBP1) (Brulé et al., 2020) and wild-type mouse major urinary protein 1 (mMUP1) (Sharrow et al., 2003). Only rOBP3-w and rOBP3-c-based biosensors were considered to study the effect of RH and immobilization strategy on selectivity. pOBP1 and mMUP1-based biosensors were then added to the chip to extend the detection range. The rOBP3-c mutant was designed as a negative control and was obtained by modifying the amino acid sequence of the rOBP3-w binding pocket. More specifically, two phenylalanine residues located at positions 90 and 103 in the binding pocket of the wild-type protein were substituted by bulky tryptophan residues, consequently blocking the binding site and rendering the protein unable to bind odorants. In addition, a segment of six histidine (6xHis-
tag) residues and a cysteine residue were added to the N-terminus of all four OBPs. The 6xHis tag was added to enable protein purification by immobilized metal affinity chromatography (IMAC) and the cysteine residue to enable protein immobilization, with controlled orientation, by direct self-assembly on the gold surface of a prism. It is important to note that the N-terminus is located on the opposite side to the entrance to the binding pocket. We thus expect that the thiol group would favor a protein orientation where the binding pocket faces upwards. In this study, the effect of the thiol group on OBP layers and biosensor selectivity was investigated by comparing OBP-based biosensors obtained using OBPs with and without a cysteine residue added to the N-terminus. It should be noted that for OBPs without cysteine residue, this amino acid residue was replaced by a serine residue. The amino acid sequence of the OBPs used in this study is given in supplementary material.

The production, purification and characterization of the recombinant OBPs was previously described in the following paper (Brulé et al., 2020). Briefly, the proteins were heterologously expressed using *Escherichia coli* M15 [pREP4] competent cells (pQE expression system, QIAGEN) and purified by IMAC. The purity of the OBPs was assessed by SDS-PAGE (see supplementary material Figure S1). In this work, for the fabrication of OBP-based chips, only pure proteins were used, whose corresponding bands were indicated with arrows in Figure S1 in SDS-PAGE analysis. Mass spectrum of wild-type rat OBP3 was given in Figure S2 as example to show its purity. Finally, the purified proteins were aliquoted and stored in phosphate buffer solution (NaH₂PO₄, 100 mM, pH 7.5) at -20°C. The binding properties of all OBPs to different VOCs were investigated by isothermal titration calorimetry (ITC). The dissociation constants (in µM) of OBPs to the three VOCs mainly used in this study are shown in Table S1 (see supplementary material). All VOC/OBP binding reactions had a stoichiometry of 1:1.

## 2.2. Fabrication of OBP-based chips

In brief, the OBP-based chip consisted of a glass prism coated with a thin layer of gold, on which a microarray of “OBP spots” was deposited. As explained in the following, OBP solutions were deposited as microdroplets on the gold surface, forming spots. Each spot corresponds to an individual biosensor.

To fabricate the chips, 25 mm right angle N-BK7 glass prisms (Edmund Optics) were used. The first step in the fabrication process is gold deposition. Metallization of the prisms was achieved by physical vapor deposition using a MEB 550-S (Plassys) electron beam evaporator, in a cleanroom environment. The metallization procedure has already been described in (Weerakkody et al., 2022). Briefly in the course of this process, the prisms were first etched with an Ar⁺ ion gun at 300 V and an etching rate of ≈1.9 nm/min for 24s to ensure increased flattening of the prism surface. After that, a thin layer of chromium (≈ 3 nm) and a thin layer of gold (≈ 54 nm) were successively deposited at rates of 0.1 nm/s and 0.25 nm/s, respectively. The role of the Cr layer was to ensure the good adhesion of the gold layer onto the prism surface (Weerakkody et al., 2020).

The second step in the chip fabrication process is plasma treatment. Prior to OBP deposition, the metallized prisms were subjected to a three-minute plasma cleaning process (75% oxygen, 25% argon, 0.6 mbar, 80 W) to remove any organic contaminants from the gold surface. This treatment was performed using a low-pressure FEMTO plasma cleaner (Diener
Electronic, Germany). Plasma cleaning renders the gold surface very hydrophilic, which becomes hydrophobic again over time. Therefore, to reduce the hydrophilicity of the cleaned gold surface, the prisms were left for 48h in a sealed box at room temperature.

48h after plasma treatment, the OBP microarray was deposited on the surface of the prisms using an automated non-contact spotting robot, sciFLEXARRAYER (Scienion, Berlin, Germany). Spotting was performed in a closed chamber at 80% RH to limit evaporation and prevent the micro-spots from drying out during the process. OBP solutions with a concentration of 8 µM, supplemented with 10% v/v glycerol, were freshly prepared just prior to deposition. For this, aliquots of stock solutions (initially stored in glass vials at -80°C) were thawed on ice to avoid protein denaturation, then diluted in a 96-well microplate to the desired concentration. Dilution buffers consisted of phosphate buffer (100 mM NaH₂PO₄, pH 7.5) mixed with glycerol. It should be noted that the concentration of OBPs in the spotting solutions was optimized in a previous study (Hurot et al., 2019). Moreover, spectroscopic ellipsometry characterization of the OBP layers suggested that at this concentration, the proteins form monolayers (see Figure S3 and table S2 in supplementary material). Glycerol was used to prevent rapid evaporation of the microdroplets. The proportion of this polyol was also optimized (data not shown). Once the protein solutions were ready, three replicates of each solution were deposited at predefined positions on the prism surface. The three replicates of the same OBP solution were randomly distributed over the chip surface to limit positional bias. Each spot consisted of 6 nL of OBP solution. The distance between the centers of two spots was set to 700 µm.

After spotting, the prisms were incubated for approximately 18h at 4°C in a humidity-controlled chamber to allow the proteins to self-assemble on the gold surface. Then, the surface of the prisms was rinsed to remove unbound proteins and dilution buffer. The rinsing process consisted in immersing the prisms in two successive ultra-pure water baths for one minute. Finally, after rinsing, the obtained chips were directly mounted in the analysis chamber for VOC analysis.

## 2.3. Experimental Bench

The bench used in this study to perform VOC injections and measure and record biosensor response can be divided into two parts: a gas-phase fluidic circuit and an SPRi setup. A detailed description of the bench with a schematic representation of the system is given in (Brenet et al., 2018; Weerakkody et al., 2022).

Briefly, the fluidic circuit consisted of two main stages through which the carrier air was passed before reaching the chip: the humidity stage and the VOC stage.

The carrier air was generated and pushed through the fluidic circuit by means of a compressed air system (zero gas system, MCZ, Germany) consisting of an air compressor with a catalytic gas purifier and a dryer.

In the fluidic system, dry air was first passed through the humidity stage, composed of two separate lines: the wet line and the dry line. The airflow in each line was controlled by a flow controller (Bronkhorst), and a high-pressure bubbler (Swageloke) was integrated into the wet line to humidify the air. By mixing the air from the two lines with varying flow rates in each, it was possible to achieve different levels of humidity in the carrier air. The total flow was maintained at 250 mL/min. Next, the carrier gas entered the VOC region, which was also
composed of two lines: the purge line (carrying clean carrier gas) and the VOC line. A hermetically
sealed container in which VOC solutions were dissolved in mineral oil was integrated into the
VOC line to create a dynamic headspace (DHS). As with humidity, by mixing air from the two lines
with different flow rates in each, we were able to obtain different VOC concentrations in the
carrier air. Total flow in this region was maintained at 100 mL/min.

Subsequently, air from the fluidic circuit entered the measurement chamber where the
chip is mounted, and the biosensor response was monitored by SPRi. A home-built SPRi setup
kept in a temperature-controlled oven (Memmert) was used. The wavelength of the excitation
light was set at 632.8 nm (Brenet et al., 2020) and the reflected light was captured by a CCD
camera. The modulation of reflectivity (ΔR) of all biosensors on the chip was monitored
simultaneously at a fixed working angle. A measurement was taken every three seconds. Further
details of the SPRi setup can be found in (Weerakkody et al., 2022). Finally, at the outlet of the
measurement chamber, the VOC concentration was measured by a photoionization detector (PID
- ppbRAE300, RAE Systems).

## 2.4. VOC injection procedure

To conduct a series of VOC injections, the experimental procedure first entailed a blank
injection, which corresponded to an injection of air containing no VOCs. Subsequently, a solution
of VOC dissolved in mineral oil was introduced into the hermetically sealed container connected
to the VOC line to create a DHS. The VOC solution typically consisted of 1 mL of white mineral oil
(Sigma Aldrich, France) in which 50 or 100 µL of VOC were dissolved depending on the number
of injections to perform. The solution was stirred continuously and the DHS was first allowed to
equilibrate for approximately 30 s, then VOC-containing air was injected into the measurement
chamber for two to four minutes, and the biosensor response was measured and recorded. At
the end of the VOC injection, pure carrier air was introduced to rinse the tubes and regenerate
the biosensors. It should be noted that all injections were performed at a flow rate of 100
mL/min. In this study, the effect of humidity on biosensor response was investigated. For this
VOC injections were performed under different RH levels including 0%, 30% and 50%. At the start
of each series of injections, two injections of 1-hexanol were systematically performed, serving
as a reference for comparison between experiments. 1-hexanol was used as a reference for
comparison between experiments. The detection of pure VOCs (octanal and IBMP) as well as rose
geranium and peppermint essential oil vapors was tested. For each sample tested, two successive
injections were performed and the biosensor equilibrium response profile to the second injection
was calculated whenever possible. We preferred to study the biosensor response to the second
injection as it is considered more reliable than the first, although the two injections often give
similar results. The choice of the three pure VOCs (1-hexanol, octanal and IBMP) was made on
the basis of ITC measurements. These measurements indicated that rOBP3-w has a good affinity
for IBMP and octanal and low affinity for hexanol which was used as a reference for comparison
between experiments since it has only non-specific interaction with all used OBPs (see Table S1
supplementary material).

## 2.5. Water sorption isotherm measurements
In this study, the hydration mechanism of OBP layers was investigated by measuring water sorption isotherms. To this end, water adsorption/desorption cycles were carried out by increasing and then decreasing the RH level in the measurement chamber. To achieve this, the flow rate of the wet line was progressively increased and then decreased in increments of 5 mL/min and 10 mL/min, respectively. The flow rate of the dry line was simultaneously modified to maintain a total flow rate of 250 mL/min. The response of the OBP-based biosensors to humidity change was recorded by SPRi, and the RH level in the measurement chamber was monitored using a HIH6100 temperature and humidity sensor (Honeywell) coupled to an Arduino board (Arduino), which was connected to the output of the chamber. The hydration process of rOBP3-w was also investigated by MD simulations. The simulations were performed following the protocol described in the supplementary material.

## 2.6. Data processing

SPRi measurements were processed using Excel software. In this study, we primarily focused on the equilibrium response of the biosensors $\Delta R_{eq}$ in order to evaluate their performance. $\Delta R_{eq}$ was calculated by averaging the variation in reflectivity over the last minute of injection.

To eliminate the effect of VOC concentration and compare the response profile of two different VOCs, for a given VOC, the equilibrium response of each biosensor was normalized by dividing it by the root mean square response of the different biosensors on the same chip (for more details, see supporting information (Weerakkody et al., 2022)). Next, the response of each biosensor to a given VOC was divided by its mean response to second injection of hexanol, which was taken as a reference negative control. Finally, the responses of the different replicates of the same biosensor were averaged. To assess the discrimination capability of our bioelectronic nose system, a principal component analysis (PCA) was performed. The processed data (i.e., the equilibrium response of each replicate normalized by the root mean square and divided by its response to hexanol) were considered to perform PCA.

# 3. Results and discussion

## 3.1. Effect of humidity on the selectivity of OBP-based biosensors

As already mentioned, in this study, the effect of RH level on the selectivity and discrimination capability of OBP-based biosensors was investigated. Only rOBP3-w and rOBP3-c-based biosensors were used for these experiments. 1-hexanol and IBMP were used to assess the selectivity of the biosensors. The response of the biosensors under RH conditions of 0%, 30% and 50%, 25°C and 100 mL/min airflow rate was investigated. The kinetic response of the biosensors to hexanol and IBMP injections performed under 30% RH as well as the equilibrium response profiles of the biosensors to the two VOCs under the different RH levels are shown in

Figure 1. For hexanol, we have presented the response of the biosensors to the first injection divided by their response to the second. The fact that the processed responses of the biosensors to hexanol are close to 1 indicates good repeatability between different injections of the same compound. For IBMP, we presented the response of the biosensors to the second injection of IBMP divided by their response to the second injection of hexanol. VOC dilutions in
mineral oil, gas dilutions and PID measurements for each injection are provided in supplementary material Table S3, S4 and S5. A schematic representation of the SPRi setup showing the OBP-based biosensor array is shown in Figure 1-f.

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
**Figure 1:** Effect of humidity on the response of OBP-based biosensors. Raw kinetic response of the biosensors to a) 1-hexanol injections and b) 2-isobutyl-3-methoxypyrazine (IBMP) injections under 30% relative humidity (RH). Processed equilibrium response profiles of the biosensors to 1-hexanol and IBMP under c) 0% RH, d) 30% RH and e) 50% RH. The "biosensor response" on the y-axis corresponds to the mean equilibrium response normalized by the root mean square and divided by the response of the biosensors to hexanol (see subsection 2.6). Error bars correspond to the standard deviation between the three replicates of each biosensor. The injections were performed under 25°C and 100 mL/min airflow rate. f) Schematic representation of the SPRi setup showing the OBP-based biosensor array.

From the graphs in

Figure 1-a, we can observe that both biosensors showed a significant response to hexanol. However, according to the dissociation constant measurements in solution, OBPs have a very low affinity for hexanol. This observation strongly suggests the presence of a pronounced non-specific signal in gaseous environment. This is also supported by the significant response of the rOBP3-c-based biosensor to IBMP, in

Figure 1-b. It is important to remember that a non-specific signal refers to any signal due to the binding of VOCs outside the binding pocket.

The graphs in Figures 1-c, 1-d and 1-e show that both biosensors give the same response to IBMP injection at 0% RH. By contrast, at 30% and 50% RH, the rOBP3-w-based biosensor gives a higher signal than the rOBP3-c-based biosensor for IBMP. These results suggest that rOBP3-w loses its selectivity at 0% RH and retains good selectivity at 30% and 50% RH. This is in line with our expectations. Indeed, when investigating the underlying structural and thermodynamic principles by which rOBP3-w achieves broad ligand selectivity, Portman et al. (2014) showed that water molecules play a critical role in driving the thermodynamics of ligand binding to OBPs. In particular, they revealed that the binding energetics are modulated by two desolvation processes namely cavity and ligand desolvation. This explains the decrease in selectivity at 0% RH.

In addition, external or surface structural water, i.e., water molecules that interact directly with the protein surface, has been implicated in protein function. In the case of enzymes, for example, it has been shown that a surface hydration threshold of around 0.2 g of water per gram of protein is required for enzymatic activity (Dunn and Daniel, 2004). It is important to note that the degree of hydration depends on the water activity (a<sub>w</sub>) or the relative humidity of the medium, which is always RH/100. However, based on the analysis of various studies of enzyme function in the presence of gaseous substrates, Dunn and Daniel (2004) concluded that for the enzymes studied, i.e. not all enzyme types, there is no hydration threshold. Nevertheless, they noted that enzyme activity increases with hydration, which they associated with a possible role of water in optimizing structure or facilitating the flexibility required for maximum activity. However, it has also been reported that an increase in humidity and hence in hydration level can lead to a decrease in enzyme stability and, consequently, a decrease in activity. Branco et al. (2009) reported studies showing that at low water activity or low humidity, enzyme activity was low and increased with increasing humidity to reach an optimum. Léonard et al. (2007) showed that the enzymatic activity of *Candida antarctica* lipase B was optimal for a<sub>w</sub> = 0.05-0.1 and the enantioselectivity was optimal for a<sub>w</sub> = 0.2.

Our results seem to be consistent with the literature, as we observed an improvement in rOBP3-w selectivity with increasing humidity (from 0% to 30% RH). Finally, based on literature studies, we expect a decrease in selectivity at high RH levels due to an increase in flexibility. We observe a slight decrease in selectivity for 50% RH. Higher RH levels should be tested to verify this hypothesis.
To get a deeper insight into the role of water in the selectivity, we investigated the hydration mechanism of rOBP3-w by MD simulations and compared the results with experimentally determined water sorption isotherms. MD simulations of the hydration process of rOBP3-w were performed according to the protocol described in (Branco et al., 2009), which is detailed in supplementary material. The experimental and simulated water adsorption isotherms are shown in Figure 2.

An analysis of the graphs in Figure 2 shows that the experimental results are consistent with the simulation results. On both graphs we can note that the amount of protein-bound water seems to increase linearly at low levels of water activity and then follows an "exponential" trend above a threshold RH. Based on this profile, we can assume that water adsorption follows the Brunauer–Emmett–Teller (BET) model (see supplementary material) and consists of two phases reflecting two different modes of interaction between the protein and water molecules. At low RH, water molecules bind to preferential binding sites on the protein surface, then these patches of water molecules form a hydrophilic network on which water aggregates "exponentially" with increasing RH.

On the other hand, the results show that the inflection point of the experimental water adsorption isotherm is around 40% RH, while the inflection point of the simulated isotherm is around 60%. This difference is most likely due to the fact that in the simulation, the hydration process of a single OBP was studied, whereas experimentally we measured the hydration of OBP layers.

Studying the hydration mechanism of *Candida antarctica* lipase B, Branco et al. (2009) obtained a similar adsorption profile, with an inflection point at 50% RH. Interestingly, the authors attempted to correlate the hydration state of the enzyme with its activity and selectivity, pointing out that the optimal water activity or RH values mentioned above both correspond to hydration levels for which the water bound to the protein surface is highly structured. Based on this, in our case, we can speculate that the optimal selectivity of our OBP can be achieved at humidity levels below 40%, where surface-bound water has a high degree of structure.

<figure><img src="image_8.png" /><figcaption>Figure 2: Water adsorption isotherms of rOBP3-w obtained a) experimentally and b) from molecular dynamics simulations. Experiments and simulations were carried out at 25°C. Plot (a) shows the adsorption isotherms of three layers of rOBP3-w</figcaption></figure>
deposited on a single chip. The black curves correspond to the fitted curves following the Brunauer–Emmett–Teller adsorption model (see supplementary material for expression). In graph (b), each black dot represents, in ascending order, the box containing 27, 64, 125, 358 and 4091 water molecules (see supplementary material). The inset represents a schematic snapshot of the sequential molecular simulation illustrating the increasing adsorption of water molecules (red and white spheres) to the surface of rOBP3-w.

## 3.2. Effect of the cysteine residue on the selectivity of OBP-based biosensors

As mentioned above, a cysteine residue was added to the N-terminus of the OBPs to enable their immobilization on the gold surface and to control their orientation on the chip. In fact, the N-terminus is on the opposite side of the binding pocket entrance. We therefore expect that the presence of this amino acid would favor the most optimal orientation, in which the binding pocket is oriented upwards, and thus allow better selectivity. To test the effect of this genetically added group on the selectivity of the biosensors, we compared the response profile of biosensors based on rOBP3-w and rOBP3-c with and without cysteine residue to injections of IBMP. The injections were carried out under 30% RH, 25°C, and 100 mL/min airflow rate. The results obtained are shown in Figure 3-a.

The histogram in Figure 3-a shows that for hexanol all OBPs give almost the same response due to non-specific interactions. The biosensors based on OBPs without cysteine give a similar response to IBMP, whereas this is not the case for the two biosensors based on OBPs with cysteine residue. Indeed, rOBP3-w-based biosensor gives a much higher response. This result indicates that there is a significant reduction in selectivity in the absence of the cysteine group. We can thus hypothesize that, as expected, the cysteine may have enabled an orientation of immobilized proteins in which the binding pocket is more accessible to VOCs. In addition, we calculated the surface density of the OBP layers with and without cysteine residue. The results are shown in Figure 3-b. The surface density values were calculated from SPRi measurements. The calculation is detailed in the supplementary material. The histogram in Figure 3-b shows that the presence of the cysteine reduces the surface density of the OBPs which confirms that this residue has a real impact on the organization of proteins on the surface and thus their orientation.

<figure><img src="image_9.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_10.png" /></figure>

Figure 3: Effect of the cysteine residue genetically added to the N-terminus of the OBPs. a) Comparison between the equilibrium response profiles of biosensors based on OBPs with and without cysteine residue. The biosensor response on the y-axis corresponds to the mean equilibrium response normalized by the root mean square and divided by the response of the biosensors to hexanol
(see subsection 2.6). The injections were carried out under 30% relative humidity, 25°C and an airflow of 100 mL/min. The concentrations of injected VOCs are similar to those provided in Table S4. b) Surface density of OBP layers with and without cysteine residue. The surface density values were calculated from SPRi reflectivity measurements. The calculation is detailed in the supplementary material. Error bars correspond to the standard deviation between the three replicates of each biosensor.

### 3.3. VOC discrimination capability of OBP-based bioelectronic nose

PCA was used to assess the discrimination capability of the bioeN. The ability of the two biosensors based on rOBP3-w and c to differentiate hexanol, octanal and IBMP was first examined. The PCA results are shown in Figure 4-a and 4-b. The bioeN has good discrimination capability. Unsurprisingly, we observe that the use of only two different biosensors is not sufficient to distinguish three different VOCs. However, very satisfyingly, the result appears to be consistent with ITC measurements and show that IBMP bind predominantly to the active biosensor namely rOBP3-w, underlining good selectivity. To improve VOC discrimination, one strategy would be to extend the biosensor array by adding new biosensors with different binding properties.

In the previous subsection (3.2), we showed that OBPs without an N-terminal cysteine residue have a different surface organization. We therefore expect biosensors based on cysteine-free OBPs to have different binding properties. We have assessed the discrimination capability of a chip incorporating biosensors based on OBPs with and without cysteine. The PCA results are shown in Figure 4-c and d. The PCA plot (Figure 4-c) shows a significant improvement in the discrimination ability of the bioeN after the addition of the two biosensors based on rOBP3-w and c without cysteine. The correlation circle (Figure 4-d) suggests that these two biosensors preferentially bind octanal, thus differentiating it from the other VOCs. We also tested the addition of biosensors involving other OBPs, namely pOBP1 and mMUP1. According to the ITC results, these OBPs have different binding properties compared to rOBP3-w. The PCA results (Figure 4-e) also show an improvement in VOC separation. It should be noted that for the PCA, we considered the response of each replicate of each biosensor on two different chips and not the three replicates on the same chip for two different injections. This highlights the good repeatability of the experiments. In addition, a preliminary test on the long-term stability showed that biosensor response was stable after one week's storage of the chip in a sealed box at 4°C.
<figure><img src="image_11.png" /><figcaption>Figure 4: Evaluation of the discrimination capability of bioelectronic nose. a) Principal component analysis (PCA) plot of the equilibrium response of biosensor array composed of rOBP3-w and rOBP3-c-based biosensors to hexanol, octanal and IBMP b) Associated correlation circle. c) PCA plot of the equilibrium response of biosensor array composed of biosensors based on rOBP3-w and rOBP3-c with and without N-terminal cysteine residue to hexanol, octanal and IBMP. d) Associated correlation circle. e) PCA plot of the equilibrium response of biosensor array composed of rOBP3-w, rOBP3-c, pOBP1 and mMUP1-based biosensors to hexanol, octanal and IBMP. f) Associated correlation circle. To carry out the PCA, we considered the response of each replicate and not the average response on two different chips. We recall that on each chip there are three replicates of each biosensor. The ellipses delineating the clusters correspond to confidence ellipses, and the large symbols indicate the center of each ellipse. The injections were carried out under 30% relative humidity, 25°C, and 100 mL/min airflow rate. The concentrations of injected VOCs are similar to those provided in Table S4.</figcaption></figure>
## 3.4. Detection of VOCs in complex medium

After shedding light on the impact of various factors on the performance of our system, we assessed the ability of the bioeN composed of rOBP3-w, rOBP3c, mMUP1 and pOBP1-based biosensors to detect and discriminate more complex samples, namely peppermint and rose geranium essential oil vapors. The PCA results (Figure 5) show a good separation. This result provides preliminary evidence that OBP-based biosensors are capable of detecting VOCs in complex media, and not just pure VOCs.

<figure><img src="image_12.png" /><figcaption>Figure 5: Detection of VOCs in complex media. Left-Principal component analysis plot of the equilibrium response of biosensor array composed of rOBP3-w, rOBP3-c, mMUP1 and pOBP1-based biosensors to rose geranium and peppermint essential oils. To carry out PCA, we considered the response of each replicate individually. The ellipses delineating the clusters correspond to confidence ellipses, and the large symbols indicate the center of each ellipse. Right-associated correlation circle. The injections were carried out under 30% relative humidity, 25°C, and 100 mL/min airflow.</figcaption></figure>

## 4. Conclusions

To sum up, our fundamental study showed that the relative humidity has impact on the selectivity of OBP-based bioeN in gas phase. Indeed, rOBP3-w loses its selectivity at 0% RH and retains good selectivity at 30% and 50% RH. We believe that the loss of selectivity at 0% is associated with the fact that desolvation processes have been shown to contribute to the binding selectivity of rOBP3-w. On the other hand, we hypothesized that optimal protein selectivity can be achieved at humidity levels below 40%, where surface-bound water exhibits a high degree of structure. However, further tests are required to verify this hypothesis. We have also demonstrated the importance of the orientation of OBPs on the selectivity of OBP-based bioeN in gas phase. By studying the effect of the cysteine residue genetically added to the N-terminus of OBPs to enable their immobilization on the gold surface and to control their orientation, we found that the presence of this thiol group improved the selectivity. Finally, with regard to
discrimination capability, we have shown that the addition of biosensors with different binding
properties can improve this feature. We have also provided preliminary evidence that OBP-based
biosensors are capable of detecting and discriminating VOCs in a complex medium. Our work
may pave the way for the use of OBP-based bioeN for the analysis of more complex samples in
diverse fields in the future.

CRediT authorship contribution statement

**Marielle El Kazzy:** Writing – original draft, Conceptualization, Methodology, Investigation, Visualization. **Maxence Lalis:** Methodology, Software, Investigation, Visualization, Writing – review & editing. **Charlotte Hurot:** Methodology, Writing – review & editing. **Jonathan S. Weerakkody:** Methodology, Writing – review & editing. **Raphael Mathey:** Resources, Data Curation, Writing – review & editing. **Christine Saint-Pierre:** Resources. **Arnaud Buhot:** Conceptualization, Methodology, Software, Resources, Supervision, Writing – review & editing. **Thierry Livache:** Methodology, Writing – review & editing. **Jérémie Topin:** Methodology, Software, Writing – review & editing. **Lucie Moitrier:** Resources, Writing – review & editing. **Christine Belloir:** Resources, Writing – review & editing. **Loïc Briand:** Resources, Supervision, Writing – review & editing. **Yanxia Hou:** Writing – review & editing, Writing – original draft, Conceptualization, Methodology, Supervision, Funding acquisition, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors gratefully acknowledge the ANR and the Labex LANEF du Programme d'Investissements d'Avenir: ANR-10-LABX-51-01 for funding the PhD scholarship of M.E.K. The authors thank Fondation Nanosciences for the scholarship of J.S.W. The authors also thank the ANR and the AID/DGA for their financial support (ANR-18-CE42-0012). SyMMES laboratory is part of Labex LANEF, Labex ARCANE and CBH-EUR-GS (ANR-17-EURE-0003). Finally, the authors would like to thank Christophe Licitra for ellipsometry measurements.

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