Carbon nanotube facilitated interface formation for enhanced

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Carbon nanotube facilitated interface formation for enhanced protein sensing in electrosynthesized molecular imprinting Na Yin, Zhen Yang, and Dong Cai ACS Appl. Bio Mater., Just Accepted Manuscript • DOI: 10.1021/acsabm.9b00692 • Publication Date (Web): 29 Aug 2019 Downloaded from pubs.acs.org on August 30, 2019

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Carbon nanotube facilitated interface formation for enhanced protein sensing in electrosynthesized molecular imprinting

Na Yin†a, Zhen Yang†*bc, and Dong Cai*c aDepartment bCenter

of Biochemistry, Baylor College of Medicine, Houston, TX 77030

for Bioenergetics, Houston Methodist Research Institute, Houston, TX 77030

cDepartment

of Physics and Texas Center for Superconductivity, University of Houston, Houston,

TX 77004

1 ACS Paragon Plus Environment

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ABSTRACT Molecular imprinting is a sensing technique that uses synthetic ligands to form high-affinity binding sites complementary to the specific analyte of interest. It has been well developed for recognition of small compounds, however, the efficacy of molecular imprinting remains challenging in protein sensing. The attributing factors are the fragility of protein molecules and their complex surface chemistry that complicate the selection of functional monomers in the imprint synthesis. In this work, we theoretically and experimentally investigated the mechanism underlying the ultrasensitive performance of a carbon-nanotube originated electrosynthesized protein imprinting sensor. By computational screening of 14 phenolic compounds and 11 conventional functional monomers in the eletrosynthetic imprinting, it was found the possible formation of interface, and its involvement in the high affinity of the imprinting was suggested. By artificially enriching the functional compounds with high binding energy to optimize interface composition in the electrosynthetic protein imprint, the protein sensing performance is correspondingly increased in the electrochemical nanosensor. To the best our knowledge, it is the first proposal of interface formation for the protein imprinting design, and the hypothesis is proved in concept.

KEYWORDS Molecular Imprinting; Protein Imprinting; Carbon Electrochemical Impedance Spectroscopy; Nanosensor

Nanotubes;

Electropolymerization,



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1. Introduction Molecular imprinting has been widely applied to the detection, separation, and purification of small molecules, and reliable protocols have been well established.1-5 However, imprinting proteins remains a challenge due to the complexity and fragility of protein molecules.6-9 Typical imprints use a small number of functional monomers (fMers) to pre-complex with the analyte of interest, then the assembled recognition sites are fixed in place by cross-linking polymerization.10 In protein imprinting, the wide variety of chemical properties among amino acid residues on a protein surface makes it impossible to select effective fMers that simultaneously participate in the multitude of potential interactions (e.g., hydrophobic, ionic, polar, hydrogen bonding, and van der Waals interaction).11,

12

To address this challenge, previous work showed that simultaneously

complexing a large variety of fMers with the protein template can help imprint diverse recognition sites of the protein molecule.13-16 However, this strategy incurs another challenge: simultaneously satisfying different reaction conditions for several types of monomers in a single polymerization system. In an alternate approach, surface imprinting and epitope imprinting is directed at protein surface or fragments rather than at intact molecules in a manner that mimics antigen presentation by epitopes in the native immune system.5,

17-19

This reduced template complexity greatly

simplifies polymer design and preparation. However, the use of partial representatives of the target protein inevitably sacrifices selectivity and specificity. Aside from classical means, electropolymerization has been used to develop highperformance protein imprinting on nanosensors.20-22 In our previous work, we developed a nanosensor through electropolymerized protein imprinting on the tips of carbon nanotubes (CNTs) array.13 The specificity and selectivity of the imprints was demonstrated by detecting various proteins, including human ferritin (hFtn), E7 biomarker protein (E7) of human papillomavirus, and


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bovine calmodulin protein (CaM), with detection limits lower than ng/L. The sensor showed significant responses to hFtn-spiked buffers under competition with horse ferritin, apoferritin, and whole protein extract from bovine muscle tissues. This same strategy, followed by Viswanathan et al., also achieved extraordinary detection of ovarian cancer biomarker CA125 in human serum samples.23 In contrast to conventional imprint methods, the electropolymerized imprinting was accomplished in a one-step reaction, in which pre-complexation and scaffold fixation were simultaneously conducted. Remarkably, complicated protein imprinting can be achieved via simple electropolymerization, and can even lead to high performance. In this study, we probed the electropolymerization process by simulating molecular interactions between protein templates and a variety of fMers, especially phenolic derivatives that were generated by a phenol electropolymerization system. This study showed that the phenolic compounds did not assemble and distribute evenly on a protein surface, but rather at specific sites, which were collectively called the interface. Therefore, we propose that the interface, created by an ensemble of weak interaction of protein and fMers, contributes to the high performance of the electrosynthetic protein imprints. To demonstrate this idea, we artificially increased the amount of a specific fMers that bound to the protein of interest to adjust the interface composition in the electrosynthesized protein imprinting nanosensor. The result showed a corresponding improvement of protein imprint affinity with the optimized interface composition. These results suggest a potential role of interface formation in the protein imprinting.



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2. Experimental 2.1 Reagents and instrumentation Phenol, 3-phenoxyphenol, CaM, ferrocene carboxyl acid (FCA), and bovine serum albumin (BSA) were obtained from Sigma-Aldrich (St. Louis, MO). Human ferritin protein was obtained from AbD Serotec (Raleigh, NC). Horse ferritin and apoferritin were obtained from MP Biomedicals (Solon, OH). Phosphate buffered saline (PBS) was obtained from Fisher Scientific (Pittsburgh, PA). Electropolymerization and electrochemical behavior of thin-films were conducted with a Reference600 electrochemical system supplied by Gamry Inc. (Warminster, PA) operating via Framework. Data analysis was conducted with Echem Analyst. Transmission electron microscopy images of PPn-coated CNTs are obtained with a JEM-2010F TEM (JEOL, Tokyo, Japan) operating at 200 kV. 2.2 Simulation of docking The simulation was performed with Autodock4, a free software tool provided by The Scripps Research Institute. As shown in equation (1), the free energy of a molecule from van der Waals forces, hydrogen bonding, electrostatics, and desolvation can be calculated as: (1) Here, i and j indicate the atom pair, Wx the weighting constants, rij the distance between atom i and atom j, and A, B, C, and D are constant parameters. E (t) is determined by deviation from ideal hydrogen bonding geometry, depending on angle t. In the third term, q is partial atomic charges, and ε is the dielectric constant. In the final term, Vx is the atomic fragmental volume, S is the solvation parameter, and σ is the Gaussian distance constant, set at 3.5 Å.


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When molecules bind, there is a change in free energy. As expressed in equation (2), the field ΔG, i.e., the free energy change, integrates the energy evaluations of molecules ( Vxy ) as well as the conformational entropy lost upon binding (ΔSconf). (2) L refers to ligand, i.e., phenolic molecules, and P refers to protein. In the computation, the root mean standard deviation (rmsd) of docking conformations was used to measure the similarity of docking locations. Docking results within the designated rmsd limit (3.5 Å) were put in the same cluster as the seed, while those with a larger rmsd value were used as a new seed to generate a new cluster. Protein 3D crystal structures were obtained from Protein Data Bank (PDB), and 3D structures of ligands were obtained from the ZINC and PubChem databases. 2.3 CNT array preparation As previously reported,13 CNT array preparation began with array fabrication; CNT were grown via plasma-enhanced chemical vapor deposition on polystyrene sphere-patterned substrates, then CNT were embedded in a SU8-2002 photoresist and then mechanically polished to expose the tips. First, SU8 was spun on an array at 3000 rpm for 30 s, followed by a soft bake for 5 min at 100°C, then SU8 was cross-linked by exposure to UV light for 3 min, and the sample was incubated at 150°C overnight. Lastly, the chip was polished with a vibratory polisher (Buehler, Lake Bluff, IL) with 80% power level for 6–9 hours until the pattern emerged from the SU8. Coating was confirmed by scanning electron microscopy. 2.4 Electrochemistry experiments A three-electrode electrochemical system was formed by connecting the CNT chip as the working electrode and using chlorinated silver and platinum wires as reference and counter 6 ACS Paragon Plus Environment


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electrodes, respectively. PPn film was deposited on the CNT array by cyclic voltammetry in PBS supplemented with 1.5 mM phenol-(pH=7.4). The potential on the working electrode was scanned five times between 0 to 0.9 V and compared to the reference electrode. To entrap CaM in PPn coating, 100 g/ml protein was added to PPn deposition buffer. Following electrophoretic attraction achieved with application of 300 mV DC voltage for 30 s, cyclic voltammetric voltages were used to form the PPn coating as described above for co-deposition of CaM. Of note, 1 mM Ca2+ was included in CaM co-deposition in order to render a fully elongated CaM conformation, which offers an imprint morphology distinct from its globular shape in the absence of Ca2+ or partially elongated conformations. EIS was conducted before and after PPn deposition to evaluate the impedance properties of the electrode surface and its interface to the buffer solution containing 1 mM FCA in PBS. The sine wave, superimposed on a 300 mV DC voltage, was 10 mV peak-to-peak in amplitude, and frequency was scanned from 1 Hz to 1 MHz. Impedance data was fit to an electrical equivalent circuit using the impedance analysis function in Echem Analyst software. DPV was conducted in the same buffer as that for EIS. Initial and final potentials were 0 and 0.5 V, respectively. Pulse size was 50 mV and pulse time was 0.05 s, step size was 2 mV, and sample period was 0.1 s. 2.5 Protein imprinting development For imprinting development, the sensor with CaM-entrapped PPn coating was rinsed and incubated overnight in deionized water at room temperature. Alternatively, a developing buffer containing acetic acid (5% w/v) and sodium dodecyl sulfate (SDS) (10% w/v) was used for higher protein extraction efficiency. To evaluate protein entrapment, an iron-containing protein, ferritin, was used as a template protein. After ferritin entrapment and removal, the sample was evaluated by TEM and EIS. To prepare TEM samples, coated CNTs were carefully scraped off with a sharp


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blade in isopropanol. About 10 l of CNT suspension was dropped onto TEM copper grids with carbon film. The samples were checked with TEM immediately after isopropanol vaporized. This specimen preparation allowed the cross-section of coated CNTs to be observed by TEM brightfield imaging, as shown in Figure 1D. Meanwhile, EDC was used to confirm the existence of iron, which originated from ferritin in imprinted CNTs (see Supporting Information Figure S1). 2.6 Ca2+ buffering for CaM measurements CaM solutions were freshly prepared each day. A 20 mg/L CaM stock solution was made in 100 mM Tris-HCl (pH=7.0), and 25 µl was used in each solution. The Tris-EGTA solution contained 1 mM EGTA and 100 mM Tris-HCl (pH=7.0) and 25 µl was added to each solution. CaCl2 stock solutions were prepared as a series of concentrations: 20 µM, 50 µM, 250 µM, 1250 µM, 6250 µM, and 31.25 mM; 5 µl of the appropriate CaCl2 solution was added to each buffer solution. The chelating of EGTA controls the free Ca2+ in the solution. Based on the dissociation constant Kd of EGTA to Ca2+ as 207 nM, the free Ca2+ for the above solution cocktail was calculated as: 4.14E-4, 1.038E-3, 5.298E-3, 2.952E-2, 3.439E-1, 2.125E3 µM. Conformation of CaM changes when it binds to Ca2+. Therefore, the various concentrations of Ca2+ promotes the exhibition of various concentrations of Ca2+-binding CaM. Prior to use, CaM-CaCl2 solutions were incubated for 20 min to allow Ca2+ and CaM binding to equilibrate. The final concentration of CaM was 10 mg/L, which is 0.6 µM based on a molecular weight of 16.8 kD.

3. Results and Discussion To begin, we conducted an in silico simulation to estimate the possible assemblies of proteins and phenolic compounds. Simulation with AutoDock4 provided a quantitative evaluation of molecular interactions, allowing us to determine the binding energy between small polymers of different building blocks and the template protein molecule, and the specific polymer binding sites 8 ACS Paragon Plus Environment


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on the template protein. We simulated several small proteins, including human papillomavirus E6 (18 kDa) and E7 (17.5 kDa) proteins and bovine testicular CaM (16.7 kDa), and focused on docking 14 regular phenolic compounds with different degrees of polymerization and structural diversity. In parallel, we also analyzed 11 commonly-used fMers for general molecular imprinting design. Equations (1) and (2) describe the force field ΔG, i.e., the free energy change corresponding to molecular interactions, by integrating the energy of molecules and conformational entropy lost upon binding. The free energy calculation includes van der Waals forces, hydrogen bonding, electrostatics, and desolvation. The ΔG indicates the affinity between the protein and polymeric compounds. The largest ΔG values from 100 runs of docking simulations of each compound on a protein are summarized in Table 1. Interestingly, electropolymerized phenolic molecules are more amenable to bind with proteins than conventional fMers. The strongest binding was observed between E7 and Compound 12 (-6.73 kcal/mol), E6 and Compound 14 (-6.25 kcal/mol), and CaM and Compound 12 (-6.15 kcal/mol). The corresponding docking of Compound 8 in CaM is shown in Figure 1A. The compound’s benzene rings occupy one of the hydrophobic pockets at the CaM surface. The hydroxyl group stably interacts with polar residues on the protein. In the simulation setting, when multiple docking simulations result in binding in the same region, a cluster is formed, suggesting that area is a potential binding site on the protein. A new cluster is recorded if the docking is displaced at least 3.5 Å from the previous site. Multiple diversified docking behaviors were observed. As shown in Table 2, the number of clusters, cluster size (i.e., number of docking sites falling in a single cluster), and ΔG values of Compounds 5 and 6 docking on CaM protein showed drastic variability. Compound 6 exhibited strong preferential binding sites on the CaM molecule. There were three clusters with a size larger than 10, and 41% of runs resulted in docking at Cluster


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1. In contrast, Compound 5 had no cluster size above 10. Consequently, the number of clusters was 36, which was more than double that of Compound 6, indicating a broad distribution and poor binding specificity on CaM. The simulation results of all of the compounds and proteins are summarized in Figure 1B. The simulation collectively demonstrated the ΔG and size of each cluster for each compound on each of the three proteins. The compounds can be compared with respect to specificity (cluster number and size) and affinity (ΔG) to the proteins. The simulation also illustrated the possible ensembles of phenolic compounds on the proteins in a broad sense. Based on their binding styles, characterized by various parameters, it seems possible that the molecules can undergo exquisite concerted interactions resulting in distinctive polymer-protein structures, similar to the pre-complexation of fMers and templates. For example, the trimer Compound 12 and dimer Compounds 6, 8, and 9 are plausible phenolic functional molecule (pfMer) candidates for E7, while dimer Compounds 2, 4, and 7 are candidates for E6, and dimer Compounds 2, 6, 8, and 9 are candidates for CaM. These binding simulations exhibited relative small cluster numbers, large cluster sizes, and high affinities in comparison to others. An important detail of the docking simulation is the spatial distribution of docking sites. In Figure 2, cluster locations of phenolic Compounds 1 and 8 on CaM are compared. The docking of phenol monomer (Compound 1) created only two relatively large clusters with the sizes of 53 and 32, and five relatively small clusters all with sizes of 15. Structural complexity of Compound 8 made it bind to more broadly distributed sites on proteins than those of the phenol monomer. A total of 20 clusters, with sizes varying from 21 to 1, occurred in the binding of Compound 8 with CaM. An unexpected result emerged when all of the clusters from docking of all 14 compounds were overlaid on the protein surface. Regardless of whether the binding forces were weak or strong,



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clusters exhibited targeting characteristics, binding common zones on E7, CaM, or E6 proteins as shown in Figure 3. Such an ensemble forms an interface on the protein surface, and it may also exert special binding properties in protein imprints. According to the Crane principle, the specificity of biorecognition between proteins is a collective effect of an array of weak interactions at the interface, rather than a few strong interactions at several non-related spots.24, 25 That is, the protein interface incorporates the spatial orientation of chemical groups, surface topological features, and forces exerted by surface amino acids. For example, the biorecognition of antigen and antibody at the interface is an “induced-fit” attributed to the flexibility of protein molecules.26, 27

Based on simulations of random polymers assembled for recognition, this biospecificity is likely

a continuous function of randomness, initially arising from purely statistical distributions of reactivity and finally evolving into precisely-defined structures.16,

28

Such structures could be

considered the polymeric interface of a template protein. Therefore, the results from the analytical and in silico studies, particularly shown in Figure 3, could be a novel interface-centric mechanism that could be used to improve electrosynthesized protein imprints. To investigate this idea, we chose a previously fabricated nanosensor as a platform to demonstrate the correlation of imprinting affinity with the interface characteristics in an electrosynthesized protein imprinting. Briefly, protein imprints were fabricated on the tips of carbon nanotubes arrays through electropolymerization of polyphenol (PPn), as shown in Figure 4. Because of the self-limited reaction of phenol electropolymerization, a thin layer of nonconductive PPn film was coated on the carbon nanotubes surface, which formed the electrosynthesized protein imprints. The binding and unbinding activities of protein targets in the imprinted cavities would lead to electrochemical signal changes in the PPn coated carbon nanotubes electrodes, as schematically shown in Figure 4B. Figure 4C and 4D structurally


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visualized the carbon-nanotubes-array originated microelectrodes array and electropolymerized PPn film on the carbon nanotubes. Figure 4E showed the recorded protein binding with the sensor using its responses to both electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) signals. Generally, the binding of protein target to the imprinted cavity increased the impedance in the EIS, on the contrary, deceased electrical current in the DPV. The detailed mechanism was explained in the previous work.13 Based on simulated ΔG values, listed in Table 1, Compound 8 was chosen as a p-fMer. As illustrated in Figure 5A, the interface in protein imprints is enhanced by artificially increasing the amount of p-fMer Compound 8 (the largest G=-4.93 kJ/mol) in the imprint. To achieve an enhanced interface, a short time for pre-complexation between p-fMer and protein template was provided. CaM was chosen as the template protein, and performance of the CaM imprint sensor was compared with and without pre-complexation of Compound 8 p-fMer. The template CaM was saturated with Ca2+ to ensure all imprinted CaM was in its elongated calcium–bound conformation (Ca-CaM). CaM sensing samples were prepared in buffers with various levels of free Ca2+ to determine the concentrations of Ca-CaM supplied to the sensor. As shown in Figure 5B, the A value denotes the relative increase in sensor impedance corresponding to target binding. This value was greatly increased in the sensor with Compound 8 pre-complexation (marked “p-fMer” in Figure 5) compared to the sensor made by regular electropolymerization (marked “phenol” in Figure 5). Hill fitting of sensor responses showed that the p-fMer sensor had a Vmax value about 10 times larger than the phenol sensor, implying a tighter binding between the p-fMer-enriched imprint, leading to a higher impedance increase, and, therefore, a higher EIS measurement sensitivity. The  value denotes the dissociation constant between the protein and imprint. For example, the imprint of the p-fMer sensor (=9.8 nM) has higher affinity than the phenol sensor



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(=50 nM). These results support the idea that imprint affinity is increased by enhancing the interface in electropolymerized protein imprints, which results from artificially increasing the amount of p-fMer.

4. Conclusions In this work, we investigated a high-quality protein imprinting developed by electropolymerization of phenol compounds, and we focused on interface formation during the imprinting. An in silico simulation was conducted to estimate the molecular interactions of a protein template with potential fMer candidates, i.e., 14 phenolic derivatives and 11 commonlyused fMers. Interestingly, the spatial distribution of the docking sites from computationally screened fMers revealed the overlap of docking sites, which seemed to form an interface on protein surfaces. The interface included ensemble molecular interactions, consisting of a variety of weak forces, and is speculated to result in the creation of high-affinity protein imprints. Experimentally, we designed an enhanced interface imprint by artificially increasing the amount of computationally screened p-fMers in nanosensors, and we successfully achieved improved performance, denoted by higher imprinting affinity. This work provides a novel and useful approach, focusing on the vital role of the interface formed on protein surfaces, to enhance protein imprinting technology.

Supporting Information. EDX spectrum of CNT tips upon the hFtn imprinting.

Conflicts of interest There are no conflicts of interest to declare.

†Both

authors contribute equally. 13 ACS Paragon Plus Environment

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*Corresponding authors: [email protected]; [email protected]

AUTHOR CONTRIBUTIONS Na Yin and Zhen Yang designed the project, performed the work, analyzed the data, and prepared the manuscript; Dong Cai Ren supervised the work, analyzed the data, and revised the manuscript.

ACKNOWLEDGEMENTS We gratefully acknowledge support from CPRIT (RP150343). References 1. Whitcombe, M. J.; Vulfson, E. N., Imprinted Polymers. Adv. Mater. 2001, 13 (7), 467478. 2. Yang, Q.; Li, J.; Wang, X.; Peng, H.; Xiong, H.; Chen, L., Strategies of molecular imprinting-based fluorescence sensors for chemical and biological analysis. Biosens. Bioelectron. 2018, 112, 54-71. 3. Mosbach, K., Molecular imprinting. Trends Biochem. Sci. 1994, 19 (1), 9-14. 4. Alexander, C.; Andersson, H. S.; Andersson, L. I.; Ansell, R. J.; Kirsch, N.; Nicholls, I. A.; O'Mahony, J.; Whitcombe, M. J., Molecular imprinting science and technology: a survey of the literature for the years up to and including 2003. J. Mol. Recognit. 2006, 19 (2), 106-180. 5. Chen, F.; Zhang, J.; Wang, M.; Kong, J., Magnetic molecularly imprinted polymers synthesized by surface-initiated reversible addition-fragmentation chain transfer polymerization for the enrichment and determination of synthetic estrogens in aqueous solution. J. Sep. Sci. 2015, 38 (15), 2670-2676. 6. Crapnell, R. D.; Hudson, A.; Foster, C. W.; Eersels, K.; Grinsven, B. v.; Cleij, T. J.; Banks, C. E.; Peeters, M., Recent Advances in Electrosynthesized Molecularly Imprinted Polymer Sensing Platforms for Bioanalyte Detection. Sensors 2019, 19 (5), 1204. 7. Verheyen, E.; Schillemans, J. P.; van Wijk, M.; Demeniex, M.-A.; Hennink, W. E.; van Nostrum, C. F., Challenges for the effective molecular imprinting of proteins. Biomaterials 2011, 32 (11), 3008-3020. 8. Chen, L.; Wang, X.; Lu, W.; Wu, X.; Li, J., Molecular imprinting: perspectives and applications. Chem. Soc. Rev. 2016, 45 (8), 2137-2211. 9. Liu, J.; Deng, Q.; Tao, D.; Yang, K.; Zhang, L.; Liang, Z.; Zhang, Y., Preparation of protein imprinted materials by hierarchical imprinting techniques and application in selective depletion of albumin from human serum. Scientific Reports 2014, 4, 5487.



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10. Turner, N. W.; Jeans, C. W.; Brain, K. R.; Allender, C. J.; Hlady, V.; Britt, D. W., From 3D to 2D: a review of the molecular imprinting of proteins. Biotechnol. Progr. 2006, 22 (6), 1474-1489. 11. Boysen, R. I., Advances in the development of molecularly imprinted polymers for the separation and analysis of proteins with liquid chromatography. J. Sep. Sci. 2019, 42 (1), 51-71. 12. Culver, H. R.; Peppas, N. A., Protein-Imprinted Polymers: The Shape of Things to Come? Chem. Mater. 2017, 29 (14), 5753-5761. 13. Cai, D.; Ren, L.; Zhao, H.; Xu, C.; Zhang, L.; Yu, Y.; Wang, H.; Lan, Y.; Roberts, M. F.; Chuang, J. H.; Naughton, M. J.; Ren, Z.; Chiles, T. C., A molecular-imprint nanosensor for ultrasensitive detection of proteins. Nature Nanotech. 2010, 5, 597. 14. Hayden, O.; Lieberzeit, P. A.; Blaas, D.; Dickert, F. L., Artificial Antibodies for Bioanalyte Detection—Sensing Viruses and Proteins. Adv. Funct. Mater. 2006, 16 (10), 12691278. 15. Yang, B.; Fu, C.; Li, J.; Xu, G., Frontiers in highly sensitive molecularly imprinted electrochemical sensors: Challenges and strategies. TrAC, Trends Anal. Chem. 2018, 105, 52-67. 16. Chen, J.; Lewis, C.; Balamurugan, D.; Yang, Z.; Ai, L.; Cai, D., Theoretical analysis of a high performance protein imprint on a nanosensor. Sensing and bio-sensing research 2016, 7, 12-19. 17. Nishino, H.; Huang, C.-S.; Shea, K. J., Selective Protein Capture by Epitope Imprinting. Angew. Chem. Int. Ed. 2006, 45 (15), 2392-2396. 18. Ge, Y.; Turner, A. P. F., Too large to fit? Recent developments in macromolecular imprinting. Trends Biotechnol. 2008, 26 (4), 218-224. 19. Chen, F.; Zhao, W.; Zhang, J.; Kong, J., Magnetic two-dimensional molecularly imprinted materials for the recognition and separation of proteins. Phys. Chem. Chem. Phys. 2016, 18 (2), 718-725. 20. Tavares, A. P. M.; Sales, M. G. F., Novel electro-polymerized protein-imprinted materials using Eriochrome black T: Application to BSA sensing. Electrochim. Acta 2018, 262, 214-225. 21. Erdőssy, J.; Horváth, V.; Yarman, A.; Scheller, F. W.; Gyurcsányi, R. E., Electrosynthesized molecularly imprinted polymers for protein recognition. TrAC, Trends Anal. Chem. 2016, 79, 179-190. 22. Wei, Y.; Zeng, Q.; Huang, J.; Hu, Q.; Guo, X.; Wang, L., An electro-responsive imprinted biosensor with switchable affinity toward proteins. Chem. Commun. 2018, 54 (66), 9163-9166. 23. Viswanathan, S.; Rani, C.; Ribeiro, S.; Delerue-Matos, C., Molecular imprinted nanoelectrodes for ultra sensitive detection of ovarian cancer marker. Biosens. Bioelectron. 2012, 33 (1), 179-183. 24. Larsen, T. A.; Olson, A. J.; Goodsell, D. S., Morphology of protein–protein interfaces. Structure 1998, 6 (4), 421-427. 25. Kastritis Panagiotis, L.; Bonvin Alexandre, M. J. J., On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J. Royal Soc. Interface 2013, 10 (79), 20120835. 26. Rini, J. M.; Schulze-Gahmen, U.; Wilson, I. A., Structural evidence for induced fit as a mechanism for antibody-antigen recognition. Science 1992, 255 (5047), 959.


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Table 1. The largest free energies ΔG [kcal/mol] of compound interactions on proteins. E7

E6

CaM

-3.65

-4.14

-3.82

2 2-(3-hydroxyphenoxy)phenol

-5.38

-5.84

-5.31


-5

-5.29

-5.12


-5.14

-5.56

-4.93


-4.22

-5.52

-4.69

6 4-Phenoxyphenol

-4.58

-4.78

-5.02

7 2-Phenoxyphenol

-4.96

-5.47

-4.9

8 3-Phenoxyphenol

-4.8

-5.28

-4.93

9 4,4'-Dihydroxydiphenyl ether

-4.68

-4.87

-5.24

10 2-(3,4,5trihydroxyphenoxy)benzene -1,3,5-triol

-5.36

-6.32

-5.7

11 1-(2,4,6trihydroxyphenoxy)benzene -2,3-diol

-5.78

-6.73

-5.56

12 3-(3-Phenoxyphenoxy)phenol

-6.73

-6.03

-6.15

13 4,4'-(1,4phenylenebis(oxy))diphenol

-5.06

-5.56

-5.79

14 2,2'-[1,4phenylenebis(oxy)]diphenol

-6.14

-6.25

-6.08

1 Itaconic acid

-4.00

-6.12

-3.05

2 Acrylic acid

-3.23

-4.61

-2.47

3 4-Divinylbenzene

-3.99

-4.34

-4.25

4 4-VP

-3.68

-4.31

-3.62

5 MAA

-3.47

-4.26

-2.84

6 2-Vinylpyridine

-3.77

-4.11

-3.64

7 Methylmethacrylate

-2.96

-3.94

-2.99

8 Acrylonitrile

-2.39

-3.84

-2.75

9 Acrolein

-2.05

-3.75

-2.34

10 AAM

-2.71

-3.73

-3.13

11 Allylamine

-3.03

-3.63

-4.46

Compound 1 Phenol

fMer


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Table 2. The cluster size and ΔG of each cluster from the simulated docking of compounds 5 and 6 with CaM. Cluster number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

CaM-Compound 5 ΔG [kcal/mol] Cluster size -4.22 5 -4.19 4 -4.15 3 -4.1 6 -3.97 3 -3.97 9 -3.95 7 -3.95 7 -3.92 2 -3.9 4 -3.89 2 -3.87 5 -3.87 2 -3.86 2 -3.86 1 -3.85 1 -3.84 2 -3.81 4 -3.79 3 -3.78 4 -3.75 1 -3.75 1 -3.72 2 -3.72 1 -3.69 1 -3.69 3 -3.66 1 -3.64 1 -3.6 4 -3.59 1 -3.58 2 -3.57 1 -3.52 2 -3.48 1 -3.44 1 -3.33 1

CaM-Compound 6 ΔG [kcal/mol] Cluster size -4.58 41 -4.38 6 -4.34 6 -4.33 3 -4.33 11 -4.3 2 -4.25 12 -4.17 6 -4.16 2 -4.02 2 -4.01 1 -4 2 -3.99 1 -3.94 1 -3.89 1



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FIGURE CAPTIONS Figure 1.

Docking of phenolic compounds on proteins. (A) Docking of Compound 8 on CaM

protein in the strongest field (G=-4.93 kJ/mol). Its location is presented in the context of the whole protein and a magnified view. The surface atoms of carbon, nitrogen, oxygen, and sulfur are displayed in gray, blue, red, and yellow, respectively. Green indicates the scope of atom displacement within the cluster. (B) Simulated docking clusters on proteins. The axes show strength of binding force fields. Symbol size is proportional to cluster size. Specifically, phenol cluster size is presented with a scale-down factor of 0.5. On each protein, the number of symbols of each compound represents the number of clusters. The yellow band in the top panel indicates the range of G from the docking of 11 regular fMer molecules on E7 protein. For each compound, data represent the docking results of 100 simulations with the template protein.

Figure 2.

Phenolic compounds 1 (A) and 8 (B) with different distributions of docking sites

on the CaM protein front- and back-sides. (C) Statistical graphs of docking sites of phenolic compounds 1 (P1) and 8 (P8) on CaM proteins with their corresponded binding energy and counts in 100 runs.

Figure 3.

Interfaces formed by phenolic compounds on proteins. Docking zones of phenolic

compounds are presented on protein front- and back-sides. Each red dot represents a docking site, i.e., a cluster. The docking zone on each protein encompasses all simulated clusters of all 14 compounds.


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Figure 4. imprints.

Ultrasensitive protein detections by the protein nanosensor with electropolymerized (A)

Electropolymerization

and

electrochemical

recording

system.

The

electropolymerization was conducted in cyclic voltammetry with the working electrode (W) potential ramping between 0 and 900 mV vs. the reference electrode (R). Pt wire was used as the counter electrode (C). (B) Principle of protein detection with the imprinted CNT nanosensor. The PPn coating is deposited at the tip of a CNT. The imprints introduce leakage current and decrease the CNT electrode impedance. The target protein binding to the imprints will “plug-in” the imprint vial in the coating and increase the sensor impedance Z(). (C) The top view of the polished CNT array. Background: SEM image of the polished array; Inset: TEM image of a polished CNT embedded in SU8. (D) The side view of a CNT tip with the electropolymerized polyphenol and entrapped hFtn by TEM. The thickness of the PPn coating on the tip 5-10 nm that is close to the diameter of a hFtn molecule. (E) The recording of protein binding with the sensor. Electrochemistry Impedance Spectroscopy (EIS) detected the sensor signals in the sequential status of the sensor: as-prepared CNT chip (no PPn) with PPn coatingafter hFtn MI developing (hFtn imprinted coating)  hFtn rebinding (10-7 g/L hFtn). Differential Pulse Voltammetry (DPV) also showed signals corresponding to MI development (no hFtn) and rebinding to targets (10-7 g/L).

Figure 5.

Protein detection with tuned imprinting affinity. (A) A scheme showing the

customized interface in the imprinting. In the upper row, CaM protein and Compound 8 are used as the template and p-fMer, respectively. After their pre-complexation, the p-fMer pre-occupies its binding sites (red spot). The later phenol electropolymerization completes the interface with other phenolic molecules by filling in the unoccupied areas (gold). Besides the addition of binding



Page 22 of 28

sites, the overall interface might maintain the same shape as that obtained in the direct electropolymerization without p-fMer modification, shown in the lower row. The illustration does not reflect exact simulation results or dimensional scales. (B) Comparison of protein detection between sensors fabricated by the two illustrated approaches. CaM was used as the template, and Compound 8 as the p-fMer. “A” values represent the relative increase in modulus of impedance (ZmodCa) due to Ca-CaM rebinding at given [Ca2+], compared to the impedance at [Ca2+]-free (Zmod0). Data were fitted with the Hill equation.


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Figure 1



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Figure 2


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Figure 3



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Figure 4


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Figure 5



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For Table of Contents Only


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