How do protein therapeutics aggregate under hydrodynamic flow?

Protein aggregation is highly problematic to the £200bn biopharmaceutical industry as it can pose dangers to the patient (loss of efficacy of the protein drug and induction of the immune response) and can increase time-to-market and, therefore, cost of goods. Over the last four years, we have shown that fluid-driven forces can drive the aggregation of a small test protein (BSA) by triggering the exposure of new protein surfaces with greater self-affinity, leading to aggregation (see Dobson et al. (2017) Proc Natl Acad Sci USA. 114:4673-4678).
The flow induced aggregation pathway(s) of biopharmaceuticals such as IgGs, however, remain unexplored and consequently industry cannot reliably identify sequences that are inherently manufacturable. As antibody drug conjugates, next generation bi-specific platforms and gene therapy vectors are already posing an even greater manufacturing challenge, aggregation remains the major threat to the realisation of the enormous potentials of proteins in the bio-technology industry.
The goal of this BBSRC Collaborative Training Partnership PhD studentship between the Astbury Centre for Structural and Molecular Biology and Medimmune, therefore, is to apply Leeds’ unique expertise in protein (un)folding mechanisms, analytical mass spectrometry and hydrodynamic flow to identify the aggregation interfaces of antibodies provided by Medimmune.

To achieve this goal it is necessary to locate the aggregation interface and evaluate current or develop tools to allow their identification. This will be achieved by:
(i) measuring the flow-induced aggregation of a test set of scFvs that vary in their aggregation behaviour under different flow conditions.
(ii) assessing the ability of current bio-informatic approaches (such as SAP, CAMSOL and SOLUBIS) to predict the rank order of aggregation.
(iii) determining the mechanism of flow-induced unfolding. This will be achieved using a powerful combination of mass spectrometric and other labelling data to benchmark and / or constrain steered Molecular Dynamics simulations.
(iv)
This fundamental study will ultimately facilitate the economic production of current and next generation high-value bio-pharmaceuticals.