Exploiting Deep Learning-based protein structure prediction for function annotation and structural biology

Description

Applications will be reviewed until a suitable candidate is appointed.

Protein structural information is crucial for an understanding of protein function and evolution. Currently, only there is only experimental data for a tiny fraction of the protein universe. However, Deep Learning methods such as AlphaFold 2 (Jumper et al., 2021) allow structure predictions for the remainder to be made with unprecedented accuracy.  These methods open up the dark proteome for structure-based function annotation and have profound implications for experimental structural biology.

The project will entail application of new Deep Learning methods for cutting-edge protein structure prediction. Applied for function prediction, the project will focus on proteins of various origins, including locally produced genomes and proteomes, but with a likely focus on families of currently unknown structure but proven medical or biotechnological interest (eg Mesdaghi et al., 2020). To achieve the fullest possible picture, a battery of structure-based function prediction methods will be applied to models produced and those data complemented by sequence- and context-derived information (Rigden, 2017). The project may, alternatively or in addition, consider the application of the structure predictions in the contexts of X-ray crystallography or cryo-EM, exploiting long-standing collaborative links to CCP4 and CCP-EM.

You will be based in Liverpool and will join a nurturing and productive group with a strong track record in structural bioinformatics, especially at the interface between bioinformatics and experimental structural biology. You will learn transferable and valuable bioinformatics skills working in an area of biology relevant to drug discovery and current health challenges.

You will have at least a good B.Sc. 2:1 in Biological or Life Sciences, or possibly in a computational subject. An interest in programming, especially with Python, is an advantage. Informal enquiries to drigden@liv.ac.uk are welcome.

The University of Liverpool has a vibrant PhD community. You can read some of their stories here https://www.liverpool.ac.uk/systems-molecular-and-integrative-biology/study/postgraduatestories/