Computational and machine learning driven development of new polysialyltransferase (ST8SiaII) inhibitors against metastatic cancer

Polysialic acid (polySia) is a carbohydrate polymer important for embryonic development. PolySia is absent in healthy adult tissues whereas overexpressed in several tumours. The high expression of PolySia-NCAM (neuronal cell adhesion molecule) is strongly associated with poor clinical prognosis and several cancers such as lung cancer, pancreatic cancer, neuroblastoma, and gliomas. The synthesis of polySia is mediated by two polysialyltransferases (polySTs): ST8SiaII and ST8SiaIV. In particular, ST8SiaII is of great importance due to its high expression in several tumours which can be thus targeted for selective inhibition, further presenting a new therapeutic opportunity to treat metastatic cancer. Our previous work has demonstrated the druggability of ST8SiaII. We already have established in vitro and in vivo models for the screening of new compounds.

The project is driven by advanced in silico modelling as following:

1. Structure-based design

Techniques such as homology modelling, high throughput virtual screening, molecular docking (HTVS), binding free energy calculations, prediction of pharmacokinetic properties and molecular dynamics simulations will be employed to screen and prioritize drug-like small molecules that could bind to the substrate binding site of ST8SiaII (relative to in-house inhibitor CMP). The promising hit compounds will be purchased/synthesized and will be tested experimentally.

2. Machine learning

Multiple machine learning models will be developed based on the chemical information of the existing ST8SiaII inhibitors. The best model will be combined with HTVS to score and screen potential hit compounds that could bind selectively to ST8SiaII over other isoforms such as ST3Gal-III, ST3GaI-IV and ST6Gal-I.

3. Development of protein – protein interaction inhibitors

ST8SiaII forms key protein-protein interactions with NCAM which are crucial for polysialylation. A consensus protein – protein docking approach will be implemented to develop ST8SiaII – NCAM interaction models, followed by oligopeptide docking and construction of pharmacophore models to eventually identify new peptides/compounds disrupting the protein – protein interaction.

How to apply

Formal applications can be made through the University of Bradford web site. Applicants should register an account, and choose 'Full-time PhD in Cancer Therapeutics' as the course.

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