Identification of pathogenic protein mutations using synthetic biology, structural bioinformatics and biochemistry

Additional Supervisor: Prof Danny Huang (University of Glasgow)

Background

Understanding how human genetic variation translates into observable phenotypic differences is one of the most important questions in all of biology and medicine. Most human genetic disorders are caused by mutations in protein-coding regions of DNA that affect the way proteins are made. While humans exhibit extensive protein sequence variation, most of it has little or no phenotypic effect. Therefore, the ability to distinguish damaging mutations from the large majority of benign variants is tremendously important for disease diagnosis, treatment and prevention.

In this project, the student will first use deep mutational scanning to measure the phenotypic effects of all possible single amino acid substitutions in proteins associated with human genetic disorders and cancer. The experimental protocol will build upon the previous success of the Kudla lab using cutting-edge synthetic biology approaches for the high-throughput characterisation of RNA mutants (Puchta et al, 2016) and the ongoing work of the Kudla and Marsh labs to adapt this to proteins.

Next, the student will perform comprehensive computational analyses of the experimental data. The positions of tens of thousands of mutations and their predicted effects on protein stability and interactions will be analysed, building on the expertise of the Marsh lab in structural analyses of disease mutations (e.g. McEntagart et al, 2016) and protein complexes (Abrusan and Marsh, 2018). Using a combination of structural bioinformatics and machine-learning, we will seek to: 1) better understand how mutations can have a damaging effect at a molecular level; and 2) prioritise pathogenic mutations that are not correctly identified by the mutagenesis experiments or computational predictors for further experimental characterisation.

Finally, the student will investigate a subset of mutants in detail, focusing specifically on mutations affecting ubiquitin signalling proteins, taking advantage of the expertise of the Huang lab in biochemical and structural studies of ubiquitin signalling proteins (e.g. Nomura et al, 2017). Ultimately, the goal is that these detailed studies will help us to understand why computational phenotype predictions often fail, and feed back into the development of improved predictive models. Moreover, these experiments, in combination with the mutational scanning and computational analyses, have the potential to provide significant novel insight into the mechanisms of these proteins in ubiquitin signalling.

Overall, this project will provide the student with a broad base of training in diverse experimental and computational techniques. This an incredibly valuable combination of skills, as it is quickly being recognised that neither computational nor experimental approaches alone are currently sufficient to confidently identify disease-causing mutations in most cases. Thus, there is a strong need for researchers familiar with multiple approaches, who can guide the choice of strategies for identification and confirmation of pathogenic variants.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine