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Surface-enhanced Raman spectroscopy detection of protein-ligand binding using D-glucose and glucose binding protein on nanostructured plasmonic substrates

1 Department of Electrical and Computer Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada
2 National Institute for Nanotechnolgy NRC, 11421 Saskatchewan Drive, Edmonton, AB, T6G 2M9, Canada

Topical Section: Nanomaterials, nanoscience and nanotechnology

Conjugated nano-biological architectures interfacing solid nano-structured surfaces with biological polymers have gained significant attention due to their potential biosensing and biocatalytic applications. However, efficient characterization of such integrated systems remains a challenge. We describe surface enhanced Raman spectroscopy (SERS) detection of complex of D-glucose with glucose binding protein (GBP) immobilized on substrates. Substrates comprised of dense Ag nanostructure arrays on Ni-coated fused silica wafers were fabricated employing ultrahigh resolution electron beam lithography. Glucose-bound and glucose-free histidine-tagged GBP was immobilized on the substrates and probed using SERS while the samples were kept in solution, and the observed Raman spectra were recorded. Three substrate designs were tested for SERS detection of the protein-ligand binding. SERS spectra of immobilized glucose-free and glucose-bound GBP exhibited pronounced differences in their Raman signatures, demonstrating the potential of SERS as a sensitive method for the detection of protein-ligand molecular recognition on a solid surface. However, morphology of the nano-patterned plasmonic structures was found to influence the SERS signatures significantly. In order to interpret the findings, simulations of electric field around the nano-structured substrates were performed. An interplay of two factors, the availability of space between Ag features where the GBP could bind to Ni, and the effectiveness of the electromagnetic enhancement of the Raman scattering in “hot spots” between these features, was concluded to determine the observed trends.
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Copyright Info: © 2017, Maria Stepanova, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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