Olfactory receptor based piezoelectric biosensors for detection of alcohols related to food safety applications

Sensors and Actuators B 155 (2011) 8–18

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Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb

Olfactory receptor based piezoelectric biosensors for detection of alcohols related to food safety applications Sindhuja Sankaran a,1 , Suranjan Panigrahi a,∗,2 , Sanku Mallik b a b

Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58108, USA Pharmaceutical Sciences, North Dakota State University, Fargo, ND 58108, USA

a r t i c l e

i n f o

Article history: Received 31 March 2010 Received in revised form 27 July 2010 Accepted 3 August 2010 Available online 27 August 2010 Keywords: Food safety Salmonella contamination Piezoelectric sensor Alcohols Molecular simulation

a b s t r a c t Our major goal in developing intelligent quality sensors is to detect bacterial pathogens such as Salmonella in the packaged beef. Olfactory sensing of specific volatile organic compounds released by the bacterial pathogens is one of the unique ways for determining contamination in food products. This work aims at developing a biomimetic piezoelectric olfactory sensor for detecting specific gases (alcohols) at low concentrations. The computational simulation was used to determine the biomimetic peptide-based sensing material to be deposited on the quartz crystal microbalance (QCM) sensor. Tripos/Sybyl® 8.0 was used to predict the binding site of an olfactory receptor and determine the binding affinity as well as orientation of the selected ligands (specific molecules) to the olfactory receptor. The designed polypeptide sequence based on the simulation program was synthesized and used as a sensing layer in the QCM crystal. The developed QCM sensors were sensitive to 1-hexanol as well as 1-pentanol as predicted by the simulation algorithm. The estimated lower detection limits of the QCM sensors for detecting 1-hexanol and 1-pentanol were 2–3 ppm and 3–5 ppm, respectively. This study demonstrates the applicability of simulation-based peptide sequence that mimics the olfactory receptor for sensing specific gases. © 2011 Published by Elsevier B.V.

1. Introduction Food-borne illness results in about 76 million cases every year costing more than six billion dollars in medical care and loss in productivity in United States [1]. Among the deaths caused by various food-borne pathogens, 31% are due to Salmonella, followed by Listeria (28%), Campylobacter (5%) and Escherichia coli (3%) [1]. Timely detection of the food-borne pathogens can aid in the prevention of food-borne disease outbreaks, thereby ensuring food safety. The microbial contamination of food products results in the production of various gaseous metabolic by-products or low molecular weight volatile organic compounds (VOCs) that are different from VOCs naturally present in the food products. The sensing of these VOCs produced by the bacterial pathogens trapped in the packaged meat headspace using an olfactory sensor system can be an advanced, rapid, intelligent, and promising technique

∗ Corresponding author. Present address: Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA. Tel.: +1 765 494 6908; fax: +1 765 496 1354. E-mail address: [email protected] (S. Panigrahi). 1 Present address: Citrus Research and Education Center, University of Florida, Lake Alfred, FL 33850, USA. 2 Present address: Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA. 0925-4005/$ – see front matter © 2011 Published by Elsevier B.V. doi:10.1016/j.snb.2010.08.003

for detecting the bacterial food-borne pathogens in food products. Our research group focuses on engineering intelligent olfactory sensors using various sensor fusion techniques to detect Salmonella contamination in packaged beef. Previous studies by our research group [2–5] on the spoiled and Salmonella contaminated packaged beef have demonstrated the potential of using the olfactory sensor systems for spoilage and contamination detection. One of our major research thrusts is to develop and evaluate novel sensing materials sensitive to indicator VOCs at low concentrations and at room temperature. The present research work focused on the development of biosensors for alcohols. In future, the developed sensors will be integrated with other sensors to develop an array of sensors for beef contamination studies. Among the various sensing materials, olfactory system based biosensors have shown enhanced potential for VOC detection at very low concentrations [6–8]. Few studies [6–12] have reported the application of biological olfactory sensing materials (olfactory receptors, odorant binding proteins, synthetic olfactory peptides) for the fabrication of olfactory sensors. The receptors present in a biological olfactory system are highly specific and sensitive to different VOCs. The olfactory receptors are sensory proteins in the olfactory system that convert the chemical signal (by binding to the odorant molecules) to electrical signal and transmit it to the brain. The olfactory receptors can be the

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most effective tool as a sensing material. The characteristics that make the olfactory receptors appropriate for biosensor applications are as follows: (i) they generate change in electrical properties within itself upon odorant binding; (ii) their molecular events can be enhanced and detected by opto-electronic devices; and (iii) their applications could be supported with genetic engineering [13]. In addition, the olfactory receptors are sensitive to a few similar odorant molecules and can identify small variations in odorants based on their structural construction and concentration. Studies have reported that odorant concentrations as low as 10 picomolar (pM) to 10 nanomolar (nM) can be detected [14] using olfactory receptor based sensors. The olfactory system-based molecules have been used for sensor development by various researchers [8,9,15,16]. However, the application of these natural biomaterials poses certain challenges in their extraction and treatment. In addition, identifying ligand specificities of the olfactory receptors may be experimentally laborious, expensive, and impractical for a large number of receptors and odorants [8]. Thus, one of the unique ways for developing olfactory sensors through simple synthesis (with high sensitivity and selectivity) is by fabricating artificial olfactory receptor sequence. If the binding site of a receptor can be determined and sequence could be derived based on this binding site, a sensor with higher specificity and sensitivity to specific odorant molecules can be developed. This process would enhance the longevity of the developed sensors as well as make the sensor development process uncomplicated. Olfactory receptor based simulated amino acid residues are being developed as sensing materials for VOC sensing [12,17]. The application of peptide sequences as sensing materials would enable the development of highly selective biosensor for detecting low VOC concentrations. In addition, the olfactory receptor sequences selective to a particular odorant offer an improved stability and reproducibility for sensor development [12,17]. In addition, they are relatively less expensive, and can provide a predictable output [17]. Lu et al. [6] developed an array of piezoelectric sensors (using four polypeptide sequences and two conducting polymer) to evaluate their sensitivities as well as selectivities to various VOCs (such as acetic acid, butyric acid, dimethyl amine, ammonia, benzene, chlorobenzene, and their mixtures). Their study reported the potential of these sensing materials for detecting and classifying different VOCs. Mascini et al. [17] developed a biomimetic olfactory receptor based sensor for detecting very low concentrations (ppb) of dioxins in food materials. Wu et al. [10] developed peptides mimicking human olfactory receptor based on molecular simulation program to detect few VOCs. The possible binding sites (polypeptide) between modeled receptor structure and target gases (trimethylamine, ammonia, acetic acid, and o-xylene) were identified and synthesized. The polypeptide chains were found to be sensitive to their respective gases. The basis for developing biosensors mimicking vertebrate olfactory system is the higher sensitivity and selectivity of the biosensors. Research is ongoing in peptide-based gas sensor development due to its inherent advantages in comparison to the application of olfactory system-based proteins (which might be time consuming to get predictable protein expressions). Based on this motivation, our research goal was to identify and evaluate potential peptide sequence for its ability to detect organic compounds of a particular functional group. In this study, olfactory receptor binding site (polypeptide) based sensors were assessed for their ability to detect alcohols. There have been very few reports on the application of polypeptides mimicking olfactory receptors for gas detection. To best of our knowledge, no studies have reported the development of the biomimetic sensors detecting alcohols at low concentrations (low ppm) in the context of food (meat) contamination. Through systematic investigation,

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we verified the applicability of peptide-based sensors for sensing application. The overall objective of this work was to develop and evaluate the performance of synthetic peptide as sensing material to detect VOCs at low concentrations. The specific objectives of this work were to: 1. Design synthetic peptide sequence using computational simulation model with a known specificity to a gas of interest. 2. Assess the performance of the designed synthetic peptide as a sensing tool for detecting desired VOC. 2. Methodology 2.1. Design of synthetic peptide A biomimetic peptide sequence was designed based on the computational modeling that will be sensitive to specific gas. The peptide sequence was designed such that it is a component of binding site of an olfactory receptor with sensitivity to a particular VOC. The methodology applied for the design of peptide sequence is summarized in Fig. 1. 2.1.1. Selection of olfactory receptor and prediction of structure The olfactory receptor was selected based on the information obtained from ‘Olfactory Receptor Database’ developed and maintained by the Shepherd Lab, Yale University, School of Medicine, USA. The olfactory receptor database is available in the website: http://senselab.med.yale.edu/ORDB/default.asp [18,19]. The database consists of human and mouse olfactory receptors along with the information of possible ligands of the olfactory receptor. The criterion for the olfactory receptor selection was that the receptor should have an affinity for alcohols (ligands) that are present in the headspace of the contaminated packaged beef. After the selection of the olfactory receptor, its primary structure in the FASTA format was obtained from Swiss-Protein database (http://www.expasy.ch/sprot/). The PredictProtein server [20] available at: http://www.predictprotein.org/ was used for predicting the secondary structure of the selected olfactory receptor. The PredictProtein predicts the structure and function of a protein (olfactory receptor) based on the similar sequences in the database. The PredictProtein server predicts the secondary structure of the protein, accessibility of the solvents, transmembrane domains, and disulfide bonds in the protein among others. One of the major interests for secondary structure prediction was to determine the transmembrane domains of the selected olfactory receptor. In PredictProtein server, ‘PHDhtm’ sub-function predicts the position and structure of transmembrane domains from sequence analysis [21]. The tertiary structure of the selected olfactory receptor was obtained from ModBase [22], a database of tertiary structures of proteins developed from comparative modeling. The threedimensional structure of the selected olfactory receptor obtained from ModBase is predicted from ModPipe, a modeling pipeline based on the programs PSI-BLAST and MODELLER [22]. Due to the absence of an experimentally determined tertiary structure of the olfactory receptors, a theoretically determined structure obtained from ModBase was used for binding site analysis as well as to estimate the binding affinity of the ligands with the olfactory receptor. 2.1.2. Identification of possible binding sites of the olfactory receptor Tripos software Sybyl® 8.0 (Tripos, St. Louis, MO, USA), a molecular modeling software was employed for determining the binding site and the binding affinities. Various modeling programs with Sybyl® 8.0 were utilized for our applications. The tertiary protein

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Fig. 1. Methodology for the design of peptide sequence.

structure obtained from ModBase was used for predicting the binding sites in the olfactory receptor. The Protein Preparation Tool in Sybyl® 8.0 was utilized to prepare the olfactory receptor protein structure prior to determining the binding sites. The hydrogen atoms and the partial charges (based on Gasteiger–Huckel method) were added to the protein structure during protein preparation. The SiteIDTM (Binding Site Analysis Tool in Sybyl® 8.0) was employed to determine the possible binding sites in the olfactory receptor. The default parameters were applied for determining the binding sites in the olfactory receptor. The grid parameters are controlled to restrict the area within the protein for binding site determination (SiteIDTM Manual, Sybyl® 8.0). The binding site obtained from SiteID program was used for docking the ligands into the selected olfactory receptor. 2.1.3. Determination of binding affinity to target compounds The docking was performed using the ‘Docking’ feature of Sybyl® 8.0. Surflex-Dock is a docking tool in Sybyl® 8.0 used for ligand–receptor docking and virtual screening of ligands. Prior to

docking, the ligands were prepared using the ‘Sketch Molecules’ function of the software. The ligands were constructed, followed by the addition of hydrogen atoms and partial charges based on Gasteiger–Huckel method. The energy minimization (100 steepest descent steps with Tripos force field) was performed to obtain minimal energy configuration of the ligands. The ligands were saved as a database in the Sybyl® 8.0. The Surflex-Dock is a scoring function that is successful in docking ligands into the binding site of the protein and eliminating false positives while retaining active compounds (Docking Suite Manual, Sybyl® 8.0). The Surflex-dock generates a protomol that is a computational representation of the binding site (where the ligands are aligned) in the protein. The scoring function positions and orients the ligands to optimize their interaction with the protein atoms. The scoring includes the hydrophobic, polar, repulsive, entropic, and solvation interactions (Docking Suite Manual, Sybyl® 8.0). The Surflex-Dock was set to provide ten best orientations of the ligands with the protein binding site. The SiteID based binding site residues was used for generating the protomol within the olfactory receptor and determining the binding affinity of the ligands in terms of CScore. The CScore is another docking tool usually used for computational drug design. The scoring function evaluates the protein–ligand configuration and strength of the relationship. The scoring functions are based on the enthalpy of binding (pair energy of the complex). The entropy of binding includes the desolvation and loss of conformational flexibility. The CScore contains a range of energy levels as calculated by different research groups: G-score—Willet’s group [23]; D-Score—Kuntz’s group [24]; PMF-score—Muegge and Martin [25]; and ChemScore—Eldridge, Murray, Auton, Paolini, and Mee [26] (CScoreTM Manual, Sybyl® 8.0). The CScore are generated by the combination of these energy scores through consensus. The G-Score is estimated based on three energy calculations: complex energy (ligand–protein), internal energy (ligand–ligand), and hydrogen bonding. The hydrogen bond energy in G-score is a complex function estimated based on the atom types and geometries of bond pairs (including metal interactions). The PMF (potential of mean force) score is a scoring function in which, a large set of complexes from Protein Data Bank (pdb) are taken and Helmholtz free energies of the interactions for protein–ligand atom pairs are estimated. In D-Score, charge and van der Waals interactions between the protein and ligand are considered. The charges in this scoring are calculated by Gasteiger–Marsili method. ChemScore considers the hydrogen bonding, metal–ligand interaction, lipophilic contact, and rotational entropy, along with an intercept term. Only single bonds are considered on rotational energy calculations. For more detail on equations, please refer the Docking Suite Manual of Sybyl® 8.0. The ligands for docking would be potential VOCs that were found in contaminated beef headspace. 2.2. Development and evaluation of quartz crystal microbalance (QCM) sensors 2.2.1. Sensing material and peptide synthesis Based on computational design, the peptide sequence was selected as sensing material (olfactory receptor binding site residues) to detect alcohols. The detailed results from computational design will be discussed in Results and Discussion section of this paper. The peptide sequence used as sensing material in this study was VFSILSPLPLIIPFVC. The peptide sequence was custom ordered from Creative Peptides, NY and used as received. The purity of the peptide sequence based on high performance liquid chromatography (HPLC) was found to be >95%. The purification of the peptide sequence was challenging due to its low solubility. The peptide sequence is hydrophobic in nature as it is a part of transmembrane domain of the olfactory receptor [27].

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Fig. 2. Experimental set-up for gas characterization (NDSU Technical Report [31]).

2.2.2. Deposition process The peptide sequence was deposited in the QCM crystal by the process of self-assembled monolayer (SAM). The thiol group of cysteine (amino acid) at the end of the peptide enabled SAM formation with the gold electrode on the QCM crystal. Thiol has a very high affinity for gold [28] as they interact through chemisorption and Van der Waals forces [29]. The QCM crystals (International Crystal Manufacturing, OK) were 10 MHz resonant frequency crystals with polished gold electrodes with a surface finish of less than 1 micron. The quartz diameter was 13.7 mm, while the gold electrode diameter was 5.1 mm. The peptide was deposited on gold electrode of QCM crystal. Peptide solution (10 mM) was prepared using dimethyl sulfoxide (DMSO) as the solvent. The QCM crystals were rinsed with acetone, methanol, and deionized water; and dried with nitrogen until the water on the surface evaporated. Piranha solution (20 ␮L, 30% H2 O2 : conc. H2 SO4 , 1:3 v/v) was placed in the gold electrode for 5 min. The piranha solution is a strong oxidizer that removes the organic materials from surface. Finally, the QCM crystals were rinsed with deionized water, followed by rinsing with 200 proof ethanol, and dried with nitrogen (until vaporization of ethanol). A set of four QCM sensors were developed for evaluating the sensors’ sensitivities to detect alcohols. Five microliter of peptide solution was deposited on the gold electrode of each side of the quartz crystal, such that peptide sequence was present in both the sides of the QCM crystals. Five microliter peptide solution was deposited on the gold electrode in two sets of 2.5 ␮L each. A 24 h time delay was observed between the depositions. After the deposition in one side of crystal, peptide solution was rinsed with solvent after 48 h. The same procedure was followed in the second side of the crystal. The peptide solution was deposited on the center of the gold electrode. The QCM sensors developed were incubated for 48 h at room temperature. After the deposition process, QCM sensors were rinsed with deionized water followed by DMSO to remove excess peptide depositions and dried with nitrogen. The change in frequency contributed by the deposition of the peptide layer was found to be in the range from 76 to 85 Hz. The QCM sensors were stored in vacuum dessicator until they were used for characterization. 2.2.3. Experimental set-up Gas sensing characterization of the QCM sensors was performed using a specially designed experimental set-up, gas sensing chamber, and data acquisition system. Fig. 2 summarizes the exper-

imental set-up used in this study. The QCM sensors were placed in a 140 mL hexagonal sensing chamber. The sensors were connected to the oscillator circuit (Standard Oscillator, International Crystal Manufacturing, OK, USA) that was in-turn connected to a frequency counter (Agilent 53131A frequency counter) using a BNC cable. Agilent (53131A) frequency counter with high stability oven base having a resolution of 0.001 Hz at 1 s gate time was used. The frequency data from the frequency counter was downloaded into a personal computer via a GPIB-USB converter. The alcohol gas concentrations (1-pentanol and 1-hexanol) were generated in a 5 L three-necked flask through liquid injection method [30]. The desired gas concentration (C, ppm) using liquid injection method can be acquired by Eq. (1): C, ppm =

10 × C0 ×  × Vvol × R × T M × Po × Vo

(1)

where, C0 is the concentration of the liquid VOC (wt. %),  is the density of the VOC (g mL−1 ), Vvol is the volume of liquid injected (␮L), R is the universal gas constant (L atm K−1 mol−1 ), T is the temperature inside the VOC preparation chamber (◦ K), M is the molecular weight of the analyte (g mol−1 ), P0 is the pressure inside the VOC preparation chamber (atm), and V0 is the volume of the VOC preparation chamber (L). The atmospheric air (in the laboratory) was used as the reference gas to simulate real-world conditions. The frequency data was collected from the frequency counter over the entire gas sensing cycle and stored in the computer. The gas sensing cycle used for the characterizing of the developed sensor was: Initial purging time: 120 s, Stabilization time in air = 240 s, Gas introduction time = 30 s, Gas retention time = 180 s, and Final purging time = 120 s. 2.2.4. Gas sensing characterization The sensitivities of QCM sensors to alcohols were measured in terms of change in frequency. The gas sensitivities were tested at room temperature. The raw data from the frequency counter was processed using a user interactive Visual C++ ‘QCM Sensor Program’ [31] developed to determine the sensitivity of the developed sensors. The logic used for normalization and analysis of frequency data obtained from sensors are summarized in Fig. 3. In the program logic, reference frequency (Fref ) indicates the baseline frequency of the QCM sensor in reference air during gas stabilization. The frequency measurement during gas stabilization and gas retention period (Fi ) is normalized to calculate normalized frequency (Fnorm ) by subtracting each of these values with that of

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Fig. 3. Data analysis logic used to analyze quartz crystal microbalance (QCM) sensor data (IPte—Initial purging time begin, INtb—Gas introduction time begin, INte—Gas introduction time end, GRte—Gas retention time end, SPte—Second purging time end).

Fref . A blank frequency (Fbl ) and its standard deviation are calculated to represent the sensitivity at 0 ppm alcohol concentration. This is important to estimate the lower limit of detection. Based on these parameters, average sensitivity is calculated by averaging Fnorm values after sensor response stabilization and maximum sensitivity is calculated by determining the maximum Fnorm after gas introduction. Fig. 4(a) shows a typical gas sensing response of a QCM sensor describing the features on the typical sensor response used in the analytical algorithm used for data analysis. Four QCM sensors were evaluated for determining the reproducibility of sensing responses among the sensors. For each QCM sensor, gas response to 10, 25, 50, 75, and 100 ppm of 1-hexanol; and 50, 75, 100, and 150 ppm of 1-pentanol were tested. For each concentration, four gas response cycles were analyzed to determine the repeatability of the sensor for a particular alcohol concentration. The lower detection limit (LDL) of QCM sensors to alcohols was estimated using Eq. (2) [32]. The LDL was calculated with 95% confidence limit: LDL =

3 × Sbl , m

(2)

where, LDL is lower limit of detection; Sbl is standard deviation of blank sensitivity, and m is the slope of QCM sensor calibration curve. 2.2.5. QCM sensor film characterization A QCM sensor was characterized with Digital Instruments DI3100 atomic force microscope (AFM, Department of Coatings and Polymeric Materials, North Dakota State University-NDSU, Fargo, ND), and JEOL JSM-6490LV High-performance scanning electron microscope (SEM, Electron Microscopic Center, NDSU). The scan areas of 1 ␮m × 1 ␮m and 10 ␮m × 10 ␮m were used. The

Fig. 4. (a) A typical gas response of QCM sensor(IPtb—Initial purging time begin, IPte—Initial purging time end, SBtb—Stabilization time begin, SBte—Stabilization time begin, INtb—Gas introduction time begin, INte—Gas introduction time end, GRtb—Gas retention time begin, GRte—Gas retention time end, SPtb—Second purging time begin, SPte—Second purging time end) and (b) Sensitivity of QCM sensors to different VOCs.

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indicates the level of significance of alignment between target and template, which is required for good modeling results. An E-value of <0.0001 is desirable during modeling. The predicted structure of the olfactory receptor was obtained from the ModBase server as Protein DataBase (pdb) file. The structure of OR744 modeled using Modbase as viewed with Sybyl® 8.0 is displayed in Fig. 6 (a). The seven transmembrane helical domains are visible in the protein structure (Fig. 6a). The pdb file obtained from ModBase was further used for docking. Fig. 5. Primary structure of olfactory receptor (Olfr744) in the FASTA format.

SEM images were taken at 25×, 1000×, and 10,000× magnifications. 3. Results and discussion 3.1. Design of synthetic peptide 3.1.1. Selection of olfactory receptor The olfactory receptor was selected from the olfactory receptor database based on the information on ligand affinity. When the ligand affinity to hexanol was selected, two mouse olfactory receptors were found to be sensitive to hexanol: ORL462 (Unitprot Q9WU87 – Olfactory receptor S3) and ORL466 (Unitprot Q8VEZ0Olfactory receptor 480). ORL462 receptor is sensitive to hexanol, heptanol, and pentanol; while ORL 466 receptor is sensitive to hexanol and heptanol. As both pentanol and hexanol are present in the headspace of the Salmonella contaminated beef package [33], ORL462 mouse olfactory receptor was selected for further simulation and analysis. The olfactory receptor S3 is a partial sequence of olfactory receptor 744 (Unitprot Q7TRM0). The sequence S3 is known to exhibit some sensitivity to aliphatic alcohols such as hexanol and heptanol [34–36]. The hexanol and heptanol yields a woody, herbal, and sweet smell [35]. The primary amino acid sequence of the olfactory receptor was obtained from the SwissProtein database available at: http://www.uniprot.org/uniprot/ [37,38]. The amino acid sequence in FASTA format obtained from Swiss-Protein database is shown in Fig. 5. The olfactory receptors structures similar to mouse olfactory receptor 744 (OR744) was searched in HORDE-The human olfactory receptor data explorer (http://genome.weizmann.ac.il/cgibin/horde/blastHorde.pl) mammalian olfactory receptor database [39–41]. It was found that in addition to the rat and dog olfactory receptors, the human olfactory receptor (OR11G2) was identical to OR744. The identities and positives based on the sequence alignment between the OR744 and OR11G2, that indicate the similarity between the sequences, were 81% and 92%, respectively. 3.1.2. Prediction of olfactory receptor structure The secondary structure of the olfactory receptor was predicted using PredictProtein. The secondary structure with possible transmembrane domain regions predicted using the model is summarized in Table 1. The reliability of the topology prediction from secondary structure prediction was high. The transmembrane domain of OR744 was comparable to the transmembrane domain of human olfactory receptor OR11G2 obtained from HORDE (Table 1). The tertiary structure of the protein was derived from ModBase Server. Bovine rhodopsin (pdb: 1L9Ha) was used as the template to obtain the structure of the OR744 olfactory receptor. Though the sequence identity was low (17%), the template was able to predict the structure of the olfactory receptors from the region 7-328 amino acid residues. A sequence of > 30% is usually desirable during tertiary structure modeling. The E-value between the OR744 and template protein was <3e−74. The E-value is a measure that

3.1.3. Binding site and affinity analysis ‘SiteIDTM ’ program of Sybyl® 8.0 was used for binding site analysis. The binding site analysis is based on the principle of solvation. The settings in the SiteID used to determine the binding site of the olfactory receptor were as follows: in grid specification – protein ˚ grid resolution 1 A; ˚ in protein-grid point distance film depth 3 A, ˚ exclusion radius 2.5 A, ˚ and population criteria – inclusion radius 8 A, minimum population of protein atoms within inclusion radius of 80; and in grid-grid distance and population criteria – inclusion radius 2–3, minimum population of grid points within inclusion ˚ and maximum population of grid points in a cluster radius of 6 A, of 300. Based on the binding site analysis, a group of amino acid residues was found to be the possible binding site for ligand binding. The amino acid residues in the binding site were as follows: Phe 4 – Ser 6 – Phe 108 – Phe 109 – Cys 116 – Leu 119 – Phe 172 – Cys 173 – Gly 174 – His – 180 – Phe 181 – Cys 183 – Leu 191 – Phe 203 – Pro 210 – Phe 215 – Ile 218 – Val 252 – Leu 254 – Phe 255 – Tyr 256 – Leu 260 – Val 261 – Tyr 263 – Ala 272 – Gly 273 – Lys 276 – Tyr 282 – Ser 283 – Leu 285 – Thr 286 – Leu 289. The binding site based on ‘SiteID’ analysis is illustrated in Fig. 6 (b). The ligands were generated using the Sybyl® 8.0 software and saved in a ligand database to be used during the docking. The docking was performed using the ‘Surflex-Dock’ program of the software. The ‘CScore’, a scoring function was used to determine the affinity of ligands to the olfactory receptor. The docking was performed in different ways to ensure the selected sequence (polypeptide chain) was a potential candidate sensing material for sensor fabrication. Firstly, the olfactory receptor was docked with the ligands using ‘SiteID’ based binding site for generating protomol. The CScore as well as ten best alignments of the ligands in conjunction with olfactory receptor were tested. The binding of the hexanol to the olfactory receptor is shown in Fig. 7. The docking using the SiteID based binding site indicated that the hexanol had an affinity to Pro-210. A study on ligand affinity of olfactory receptors [42] has summarized the possible binding site residues of different olfactory receptors. Their work reported that one of the main binding pocket residues for OR744 olfactory receptors could be Pro-210. The findings from their study [42] were comparable to that of the simulation program output of this work. The other possible binding site residues of OR744 reported by Khafizov et al. [42] were: phenylalanine (109), serine (113, 258), cystiene (116), lysine (209, 254), proline (210), valine (261), and methionine (262). Further, to explore the different possibilities, automatic protomol generation was utilized to determine the binding affinity and binding amino acid residues for hexanol binding. Similar results were obtained. The CScores illustrating the receptor affinity to ligands are summarized in Table 2. Based on the above results, the potential amino acid based sequence (210–216) for developing sensing material was selected as VFSILSPLPLIIPFVC. Cysteine (contains thiol group) was added at the end to enable self-assembly during the deposition of amino acid on the gold electrode. The CScore is a combination of different energy calculations as explained in the methodology section. The CScore represents the maximum change in energy after the binding of the ligand to the

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Table 1 Possible transmembrane domain regions in the olfactory receptor OR744. TMa

OR744

TM1 TM2 TM3 TM4 TM5 TM6 TM7 a

OR11G2

TM domain

Amino acid residues

TM domain

Amino acid residues

30-53 63-80 103-122 146-166 201-225 246-263 279-296

LLFVLFSIVYLLTLMGNASIICAV MYLLLANFSFLEIWYVTS LQFYFFFSLGSTECFFLAVM NILVISCWILGFLWFPVPIII LVFSILSPLPLIIPFVFIMGSYTLV GSHLAVVALFYGSVLVMY TLFYSVLTPLLNPVIYSL

28-54 65-87 102-122 145-165 202-223 245-269 275-294

QILLFVLFTVVYLLTLMGNGSIICAVH ILLANFSFLEICYVTSTVPSMLA FLQFYFFFSLGSTECFFLAVM CTNLVVNCWVLGFIWFLIPIV VFSVLSPLPVFMLFLFIVGSYA CGSHLAVVSLFYGSVLVMYGSPPSK QKTVTLFYSVVTPLLNPVIY

Transmembrane domain (TM).

Fig. 6. (a) Structure of OR744 mouse olfactory receptor and (b) binding site of OR744 mouse olfactory receptor.

Fig. 7. Docking of hexanol into the tertiary structure of olfactory receptor.

Table 2 CScores (binding affinity) obtained from protein–ligands docking. Rank

1 2 3 4 5 6

SiteID based docking

Automatic docking

Ligand

Total CScore

Ligand

Total CScore

Hexanol 2-Pentanol 1-Pentanol Ethanol Acetone Acetic Acid

3.84 3.59 2.69 2.29 2.22 2.00

Hexanol 2-Pentanol 1-Pentanol Ethanol Acetone Acetic Acid

3.78 3.60 2.82 2.48 2.33 1.94

protein structure. In other words, the overall energy of complex (ligand–protein) with that of individual energy of ligand and protein is compared to determine whether the binding of the ligand to the protein is preferred. The CScore results indicated that the olfactory receptor has a good affinity to alcohols, especially hexanol. The results were comparable to the findings found by other researchers [32–34]. The peptide was synthesized and further utilized as sensing material for gas sensor development. 3.2. Development and evaluation of QCM sensors 3.2.1. QCM sensor gas sensitivity to alcohols The gas sensitivities of the sensing materials were tested (100 ppm concentrations) for acetone, acetic acid, hexanol, and

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Fig. 9. QCM sensor responses to 100 ppm of 1-hexanol.

Fig. 8. Calibration curves of QCM sensors based on maximum sensitivities. Sensor response to (a) 1-hexanol and (b) 1-pentanol.

pentanol (Fig. 4b). The QCM sensors showed higher sensitivity to high molecular weight alcohols (hexanol and pentanol) than to VOCs such as acetic acid and acetone. The selectivity analysis was performed to verify the simulation results. In this regard, we were able to achieve the desired sensitivity to target VOCs. The QCM sensors were found to be sensitive to 1-hexanol as well as 1-pentanol. When the four sensors were characterized for its sensitivities to increasing concentrations of 1-hexanol, a linear response was observed. All the four QCM sensors showed good sensitivities to 1-hexanol from 10 to 100 ppm. The calibration curves of the QCM sensors based on the maximum sensitivities are illustrated in Fig. 8. The correlation coefficients (r) of the calibration

curves were found to be >0.87 indicating a good correlation of the QCM sensors’ sensitivities with 1-hexanol. The slopes of the calibration curves were approximately 0.05 Hz/ppm of hexanol. The calibration curves of QCM sensors based on average sensitivities displayed a slope of 0.04 Hz/ppm of hexanol and r >0.93. The gas sensitivities of the sensing materials were also evaluated for detecting 1-pentanol. The QCM sensor responses to 1-pentanol were lower than that to 1-hexanol (Fig. 8). The gas sensitivities of the QCM sensors were directly proportional to the concentrations of the alcohol. The slopes of the calibration curves for 1-pentanol sensing based on maximum and average sensitivities were approximately 0.03 and 0.02 Hz/ppm, respectively. Table 3 summarizes the parameters determined from the linear calibration curves for 1-hexanol and 1-pentanol detection, respectively. From the table, it could be observed that maximum sensitivities based calibration curves provided higher slopes than average sensitivities based calibration curves. 3.2.2. Repeatability and reproducibility of QCM sensors The QCM sensor response of each sensor was estimated four times for each concentration of alcohol to evaluate the repeatability of the QCM sensors. Fig. 9 displays the QCM sensor response to 100 ppm of 1-hexanol. It could be observed that the QCM sensor response to alcohol was repeatable for a particular concentration of alcohol. The QCM sensor responses to low concentration of alcohols (10 ppm) were more variable (lower repeatability) than high concentrations of alcohols (100 ppm). The QCM sensors exhibited good reproducibility to alcohols vapors among the QCM sensors (Fig. 8). Table 3 summarizes the slopes of calibration curves, correlation coefficients, and the estimated LDLs of the four sensors. The QCM sensors showed a very good reproducibility among the

Table 3 Slopes and regression coefficients of the calibration curves, and estimated LDLs based on average and maximum sensitivities for 1-hexanol and 1-pentanol. Sensors

Average sensitivity

Maximum sensitivity

Slope (Hz/ppm)

r

LDL (ppm)

Slope (Hz/ppm)

r

LDL (ppm)

1-Hexanol QCMI QCMII QCMIII OCMIV Average Std. Dev.

0.05 0.04 0.05 0.04 0.04 0.003

0.97 0.94 0.94 0.93 0.94 0.02

2.5 2.2 2.7 1.8 2.3 0.4

0.06 0.06 0.05 0.06 0.06 0.004

0.94 0.91 0.87 0.91 0.91 0.03

2.0 1.6 2.4 1.3 1.8 0.5

1-Pentanol QCMI QCMII QCMIII OCMIV Average Std. Dev.

0.03 0.03 0.02 0.02 0.02 0.007

1.00 0.99 0.91 0.95 0.96 0.04

3.0 2.9 5.3 8.9 5.0 2.8

0.04 0.03 0.03 0.03 0.03 0.005

0.98 0.98 0.94 0.94 0.96 0.02

2.4 2.6 4.2 4.9 3.5 1.2

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responses to 1-hexanol as well as 1-pentanol. The reproducibility among QCM sensor calibration curves was higher for the maximum sensitivity data than that among average sensitivity data. 3.2.3. Lower detection limit of QCM sensors The estimated LDLs were calculated from the calibration curves of the QCM sensors. The estimated LDLs for 1-hexanol detection based on average and maximum sensitivities were 2.3 ± 0.4 and 1.8 ± 0.5 ppm, respectively. The corresponding estimated LDLs for 1-pentanol were 3.5 ± 1.2 and 5.0 ± 2.8 ppm, respectively. The maximum sensitivity yielded a lower LDL due to higher slopes of the calibration curves. This study demonstrated the potential of using olfactory sensors based on olfactory receptor for detecting specific gases at room temperature. The low estimated LDL (2–5 ppm) of QCM sensors is desirable for meat contamination detection applications. Considering the estimated LDL, the QCM sensors could be applicable for determining low alcohol concentrations in packaged meat headspace. 3.2.4. Comparison of QCM sensor responses to alcohols The QCM sensors’ responses were higher to 1-hexanol than to 1-pentanol. The sensor responses to both the gases are shown in Fig. 10 (a). It can be observed that for the same concentrations of alcohols, the gas sensitivities in terms of both average and maximum sensitivities were higher for 1-hexanol detection than those for 1-pentanol detection. Comparing the QCM responses to 1-pentanol and 1-hexanol, it was found that though the correlation coefficients of calibration curves for 1-pentanol were high (0.95), the slopes were half as that of QCM responses to 1-hexanol. For these reasons, estimated LDL of QCM sensor for 1-pentanol detection was higher than that for 1-hexanol detection (Table 3). Based on the simulation results (energy calculations), the peptide sequence used as the sensing material had a greater affinity to 1hexanol than to 1-pentanol. Thus, the QCM sensor responses for the two alcohols validate the findings from the simulation program.

Fig. 10. QCM sensor responses to 1-pentanol and 1-hexanol. (a) Response curve to 75 ppm alcohol, and (b) principal component analysis plot based on maximum sensitivities of QCM sensors to 1-pentanol and 1-hexanol.

Fig. 11. Height (a and d), phase (b and e), and amplitude (c and f) atomic force microscope images of the QCM sensor. Images were taken with 1 ␮m × 1 ␮m (a–c) and 10 ␮m × 10 ␮m (d–f) scan areas.

S. Sankaran et al. / Sensors and Actuators B 155 (2011) 8–18

In addition to the gas sensitivity analysis, principal component analysis (PCA) was performed using statistical application software SAS (SAS 9.2, SAS Institute Inc. Cary, NC) on the QCM sensors’ sensitivities (maximum sensitivity) to 1-pentanol and 1-hexanol. The PCA reduces the redundancy of the QCM sensor sensitivities in each alcohol sensing gas and orthogonally projects the two classes on the principal plane to observe the discrimination of sensor responses for two alcohols. The maximum sensitivities of four QCM sensors to two alcohols were considered as features. The original input feature dataset (36 × 4 sensors) to PCA analysis had 36 samples as rows (2 alcohols × 4–5 conc./alcohol × 4 response cycles/conc.) and four sensor sensitivities as four columns. Two principal components (PCs) accounted for 96% variation of the two classes (1-hexanol and 1-pentanol) of compounds. The PC plot depicting two principal components of the alcohol groups and their respective concentrations is illustrated in Fig. 10 (b). Though there was an overlap between 10 ppm (1-hexanol) and 75 ppm (1pentanol) (sensor response to 1-hexanol could be an outlier), the PC plot indicated a good differentiation of the two alcohol groups demonstrating the ability of the QCM sensors to detect 1-pentanol and 1-hexanol.

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approach for predicting peptide sequences that could be used to detect specific gases at low concentrations. Additional studies in validating the sensors performance in real meat package are needed. In addition, other methods of computational simulation techniques for predicting the binding chemistry and behavior may be developed and evaluated. Acknowledgements We extend our sincere gratitude to USDA-CSREES for their financial support. We would also like to express our gratitude to Dr. Dongqing Lin, Mr. James Moos, Ms. Heidi Docktor, Mr. Scott Payne, and Mr. Wyatt Goettle for their help in the completion of this work. Our special thanks goes to Dr. Senthil Natesan and Dr. Stefan Balaz from the Department for Pharmaceutical Sciences for their assistance in the completion of this work. We would like to thank Mr. Darell Brehm from International Crystal Manufacturing; Mr. Chris Cranfield, Mr. Steve Manion and Mr. Dan Reitz from Agilent Technologies; and Mr. Power Cheng from Creative Peptides for their technical assistance. References

3.2.5. Characterization of QCM sensor film The AFM and SEM images were acquired to determine the surface morphology of the film layer on the QCM sensor. Fig. 11 illustrates AFM images at 1 ␮m × 1 ␮m and 10 ␮m × 10 ␮m scans. AFM images exhibited a uniform thin film formation (Fig. 11). At 10 ␮m × 10 ␮m scan areas, there were few protein aggregates that were found on the surface of the film. In addition to AFM images, SEM images (data not shown) were also acquired at 25×, 1000×, and 10000× magnifications to determine the morphology of the QCM sensor surface. At 25× magnification, the entire peptide deposition on the gold surface was visible. At 1000× as well as 10,000× magnifications, smooth peptide sensing film could be visualized. The SEM images were consistent with the findings from AFM images. 4. Conclusions This work evaluated the potential of QCM based biomimetic sensors for their sensitivities to 1-hexanol and 1-pentanol, some of the specific gases indicative to Salmonella contamination in beef. The sensing material (mouse olfactory receptor) was designed based on the simulation program (Sybyl® 8.0). The binding site with affinity for alcohols was predicted. The peptide sequence in the binding site was chosen as the sensing material based on the estimation of binding energy determined from the simulation program. The peptide sequence VFSILSPLPLIIPFVC, determined from the computer program, was used as the sensing material in the QCM sensors. Once the peptide sequence was designed and synthesized, it was deposited on a QCM crystal and the sensitivities of QCM sensors to alcohols (1-hexanol and 1-pentanol) were experimentally validated. The developed biomimetic QCM sensors exhibited good (lower ppm level) sensitivities to the 1-hexanol and 1-pentanol at room temperature. The estimated LDLs (lower detection limits) for 1hexanol and 1-pentanol detection were 2–3 ppm and 3–5 ppm respectively. The QCM sensors exhibited a linear relationship (with high correlation coefficients) between sensor sensitivities and concentrations of alcohols. The biomimetic QCM sensors also demonstrated repeatability to each alcohol and reproducibility among the sensors. Thus, the QCM sensors developed for the detection of low ppm alcohol concentrations has potential to be used as a part of integrated olfactory sensing system for contaminated meat package. The study also demonstrated the usefulness of simulation

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Biographies Sindhuja Sankaran received her M.S. in Environmental Engineering from Iowa State University (ISU), in 2006. She joined Bio-imaging and Sensing Center, North Dakota State University (NDSU) to pursue her Ph.D. focusing on development and evaluation of novel sensing materials for detecting contamination in food. She received her Ph.D. in Agricultural and Biosystems Engineering, NDSU in August, 2009. She is currently working as a postdoctoral research associate at Citrus Research and Education Center, University of Florida with major research emphasis on optical sensors for disease detection in citrus trees. Suranjan Panigrahi received his Ph.D. from Iowa State Univeristy, Ames, IA, USA. He served as a professor in the Agricultural and Biosystems Engineering Department, NDSU, Fargo, ND (until Fall 2099). He was also the director of Bio-imaging and Sensing Center, North Dakota State University, Fargo, ND, USA. He is currently a professor in the Department of Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN, USA and leads the “Integrated Smart Sensing and Solutions Laboratory”. His research focuses on machine systems engineering, and development/evaluation of intelligent sensors/sensing systems for different biological applications. Sanku Mallik received his Ph.D. from Case Western Reserve University. He is a professor in the Department of Pharmaceutical Sciences, NDSU, Fargo, ND, USA. His research focuses on design of isozyme-selective inhibitors for matrix metalloproteinase-9, fabrication of hybrid liposomes with triple-helical collagen peptides, and development of chemical receptors for proteins based on polymerized liposomes.