Antibody therapeutics have become a major class of medicines for treating a wide range of diseases. An emerging class of antibody therapeutics is nanobodies, which are small, single-domain antibodies that occur naturally in camelids and sharks. Advances in computational methods, particularly machine learning and protein structure prediction, are rapidly expanding our ability to design nanobodies for specific target antigens.
In this project, we will apply machine learning tools to identify and rationally design nanobodies that target a key protein from the malaria parasite. The designed nanobodies will be produced experimentally and evaluated for their ability to bind the target protein and inhibit parasite function. Promising nanobodies can then be iteratively refined through computational optimisation and experimental validation, creating a design–build–test cycle to improve their binding affinity and functional activity.
Skills: Computational protein design, Protein Purification, Biophysical characterisation, Structural biology
References:
Dietrich, Science 2025 389:eady0241
D Sa, J Biol Chem 2025, 301(3):108290
D Sa and Chmielewski, bioRxiv 2026, https://doi.org/10.64898/2026.05.20.726696
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