Integrating computational analyses of protein structure, protein interactions and molecular evolution to understand how mutations cause human genetic disease and drive cancer

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.
This project will build on the considerable expertise of the Marsh lab in studying the close relationships between protein structure, dynamics, assembly and evolution [1-2]. Since moving to the MRC Human Genetics Unit, we have been focusing on how these properties can have tremendous power for revealing the molecular mechanisms underlying pathogenic mutations [3-4]. In this project, the student will perform a series of systematic computational analyses, investigating how the structures and interactions of human proteins are related to patterns of genetic variation observed in the human population, in cancer, and in evolution. These analyses will utilise a variety of structural bioinformatics and molecular modelling approaches, and incorporate diverse proteomic and genomic datasets. In particular, this project will focus on understanding how different mutations can have different phenotypic effects due to loss-of-function, gain-of-function or dominant-negative molecular mechanisms. Ultimately, the goal will be to identify features of proteins and protein complexes that can be integrated with machine-learning strategies in order to improve the accuracy of computational phenotype predictions, and can help directly in the diagnosis of genetic disorders. Moreover, as part of this project, the student will also work closely with our clinical geneticist collaborators in order to help understand and explain recently identified mutations.

Applicants should hold at least an upper second class degree in a relevant subject and comply with English language requirements (see application page).

To apply for this project, please visit: https://www.ed.ac.uk/mrc-human-genetics-unit/graduate-research-and-training/integrating-computational-analyses

Application Deadline: 1st December 2018