Development of nanobody-based surfaces for on-demand smart switchable biosensing

Fialho Simões, Bárbara Sofia (2022). Development of nanobody-based surfaces for on-demand smart switchable biosensing. University of Birmingham. Ph.D.

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Abstract

The biosensing field has been evolving with the advances of nanotechnology, medicine and with the need for diagnostics and health monitoring. Biosensors are facing high demands related to sensitivity, long term stability and on demand sensing. Control over detection provides the opportunity for on-demand biosensing and smart materials such as stimuli-responsive surfaces that can control a wide range of bio-interactions. However, there are few smart surfaces for on demand biosensing and these are limited to control over simple molecular structures. To address these challenges, this research used single domain antibodies, known as nanobodies (Nbs), with great potential as biorecognition elements for sensors due to their small size, specificity and robustness. Different strategies for Nbs immobilisation on gold sensors were optimised to retain their maximum functionality. The experimental data and detailed computational simulations confirmed the formation of stable, well-oriented nanobody monolayers. Furthermore, this work explored challenges related with Nbs’ orientation and antigen dimensions, emphasizing crucial factors to consider when designing nanobody-based biosensors. Envisioning new smart electrically-responsive surfaces, preliminary studies showed the stability and high response of Nbs under applied potentials. Additionally, electrically-responsive oligopeptides, potential switching units for nanobody-antigen binding control, were designed and investigated. These oligopeptides are required to conceal the Nbs binding site, only exposing it to bind the antigen upon applied potential.

In conclusion, this work provides a step forward with the vision of combining stimulus-responsive surfaces with nanobodies for on-demand biosensing, contributing for the design and fabrication of stable, reliable, and robust biosensing platforms for a wide range of medical, biotechnological, environmental, and food applications.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):