Expanding nanobody functions through unnatural amino acid incorporation

About this opportunity

Nanobodies are an emerging class of diagnostic and therapeutic proteins derived from unique camelid antibodies. They represent one of the fastest-growing classes of biological molecules with a global market share of NZD 600 million and an exceptional expected annual growth rate of nearly 25%. Whereas traditional monoclonal antibodies used in the clinic are very large glycoproteins, nanobodies are approximately 1/10th the size but still retain the capability to bind a vast array of targets with very high affinity. Their small size, reduced complexity, and low production costs have made them very attractive as alternatives to full-length monoclonal antibodies, particularly in applications where deep tissue penetration is desirable (e.g. imaging of solid tumours).

Despite having several exceptional biophysical properties, such as a high degree of pH stability, nanobodies are inherently constrained by the limitations imposed by access to the 20 canonical amino acids typically used in biological systems. For example, nanobodies remain susceptible to degradation in protease-rich environments (e.g., the gastrointestinal tract), making them typically unsuitable for oral delivery applications. This project seeks to resolve a number of these limitations by incorporating greater functional diversity into full-length nanobody structures using unnatural amino acids. Creating new nanobody sequences is highly interdisciplinary and bridges the disciplines of immunology, protein engineering, computational biology, materials science, and chemistry.

Location

Hamilton Campus

Scholarship Value

This project is funded by a University of Waikato Seeker Doctoral Scholarship that provides a stipend of up to $30,000 per year for up to 36 months + domestic tuition fees and Student Services Levy.

The general Higher Degrees admission criteria apply.

Additional eligibility criteria specific to this project include:

• Experience with protein display systems such as bacteriophage
• Nanobody expression and purification skills
• Knowledge of peptide chemical synthesis
• Antibody analysis techniques including ELISA, Biacore and CD
• Knowledge of R or Python for NGS data processing is not essential but preferred