(Invited) Creating Optical Nano-Biosensors from Engineered Antibodies for Protein Detection

Abstract

Biosensors based on fluoro-labelled antibody fragments combine the recognition and transduction element into the same molecule leading to real-time result detection and reducing the need for laborious, multi-step assays. The key challenge is the efficient site-specific modification of antibodies with environmentally-sensitive fluorescent dyes, without affecting binding functionality. Fluorescence labelling via unnatural amino acids (UAAs) is a relatively new and highly efficient method for 100% efficient site-specific fluorescence labelling, and can be genetically incorporated into any permissible site during protein synthesis. Although over 100 UAAs have been incorporated into various proteins for diverse applications including antibody drug conjugates and bispecific antibody development using Fab, to date none of the UAAs has been incorporated into scFv for biosensing applications nor has this been used for detection of large biomolecules (e.g. protein).We demonstrate that incorporation of environmentally sensitive fluorescent UAA (Anap) into a permissible site of antibody fragments (e.g. anti-EGFR scFv) can be used for detection of target binding by monitoring the wavelength and/or intensity changes in emission spectra. A mutation screen was initially performed in order to identify the Anap mutation site that yielded the largest spectral change. We found that, across two different protein/antibody case studies, that only relatively hydrophobic amino acids within the binding interface could be mutated to generate optically-reactive species, and that the affinity of the mutants was not significantly affected. When immobilising these antibody fragments onto various surfaces (e.g. silica, carbon nanotubes, polymeric nanoparticles) for solid-phase biosensor development, we also observed unique behaviour indicative of the local hydrophilicity of the surface environment, which may be advantageous in identifying optimal surfaces for high specificity and sensitivity in complex biological environments. Here we will present our latest results on generalising this approach for protein detection in complex biological environments, and also to outline the challenges and opportunities for future developments Figure 1