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Protein structure, molecular mechanisms and human genetic disease: beyond the loss-of-function paradigm


MRC Human Genetics Unit

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Dr J Marsh No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

A 4-year funded PhD studentship in computational biology is available to join the Marsh lab(https://www.ed.ac.uk/mrc-human-genetics-unit/research/marsh-group) at the MRC Human Genetics Unit to work on the ERC Consolidator Grant research programme PROT-STRUCT-DISEASE.

The ability to identify damaging genetic variants is central to the diagnosis, treatment and prevention of human disease. Computational variant effect predictors are widely used for prioritising likely pathogenic mutations, but their utility is limited by their accuracy. Conversely, experimental characterisation of variants is powerful but time consuming and difficult to perform on a large scale, limiting applicability in routine variant prioritisation. In this project, we will improve our ability to identify pathogenic variants through a combination of computational and experimental approaches. Fundamental to our strategy will be our consideration of alternate molecular mechanisms by which mutations can cause disease, in contrast to the current overwhelming focus on loss-of-function.

First, we will investigate the different molecular mechanisms underlying pathogenic mutations, and learn how they are related to protein structure and phenotype. Next, we will perform deep mutational scanning (DMS) on at least 10 human disease genes, enabling us to measure fitness and elucidate molecular mechanisms for all possible single amino-acid substitutions. This will facilitate the direct identification of novel pathogenic variants, and allow us to evaluate the performance existing computational phenotype predictors. Finally, we will implement our own computational variant prioritisation pipeline and meta-predictor, using our new understanding of molecular mechanisms to integrate computational phenotype and stability predictors and DMS data with structural and other protein-level features. Crucially, we will demonstrate the utility of our approach in application to sequencing data from clinical and population cohort studies. Together, the knowledge we learn, the experimental data we measure, and the tools we develop will improve our ability to identify novel pathogenic variants, and thus diagnose human genetic disorders.

Potential PhD projects are available for various aspects of this research programme, including structural bioinformatics analyses of disease mutations, investigation into how protein complex stoichiometry and assembly can influence the phenotypic effects of mutations, and analysis of deep mutational scanning and variant effect predictor datasets. Interested students are encouraged to contact Dr Joe Marsh ([Email Address Removed]) with any questions and to discuss project details.

Applicants should hold at least an upper second-class degree in a relevant subject and comply with English language requirements (https://www.ed.ac.uk/studying/postgraduate/applying/your-application/entry-requirements/english-requirements).


References

Livesey BJ & Marsh JA (2020) Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Molecular Systems Biology 16:e9380
Gerasimavicius, Liu X & Marsh JA (2020) Identification of pathogenic missense mutations using protein stability predictors. Scientific Reports 10:15387
Bergendahl LT et al (2019) The role of protein complexes in human genetic disease. Protein Science 28:1400-1411
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