Predicting the Colloidal Properties of Recombinant Protein in Solution Using Molecular Dynamics Simulations

University College London

Recombinant proteins are increasingly being used as biotherapies and vaccines to treat or prevent a range of different diseases. Protein behaviour in solution can directly affect how they are manufactured and stored, and how effective they are after administration to patients. The colloidal properties of recombinant proteins including the propensity for protein-protein aggregation is therefore of significant interest to the biotech industry. 

Aggregation is increasingly thought to occur through the partial unfolding of protein structure to expose sites that are more prone to self-interaction. Overall, the self-association of proteins is influenced by a combination of surface properties that determine colloidal stability and propensity for surface interactions, the extent and kinetics of global and local unfolding, and the solvent accessibility and aggregation-propensity of local sequences.  All of these are modulated by the physical environment provided by the formulation, which can thus alter both the kinetics and dominant pathways of aggregation. 

This project will look to build on existing molecular dynamics simulation models to further deepen our understanding of the intrinsic (e.g., protein) and extrinsic (e.g., process & formulation) factors that impact protein behaviour in solution. The project will further explore the utility of predictive AI and ML across a range of conditions. 

UCL is one of the top UK research universities. The four-year PhD project is funded by an industrial biotechnology sponsor, and will be linked to the Future Targeted Healthcare Manufacturing Hub at UCL (

This multidisciplinary project would suit a candidate with a Life Science or Engineering degree with interests in biotechnology, modelling, data mining or programming. The Department of Biochemical Engineering offers PhD skills and sector training in the field of bioprocessing and biotechnology. The application deadline is 10th July 2024

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