Structural analysis and prediction of dominant negative mutations in protein complexes associated with developmental disorders

Many developmental disorders are caused by rare inherited or de novo mutations in protein- coding genes. With the huge reduction in the cost of sequencing, it is now becoming feasible to identify causal mutations in many cases. For example, the ongoing UK- and Ireland-wide Deciphering Developmental Disorders (DDD) project seeks to identify the genetic causes for >12,000 children and adults with previously undiagnosed developmental disorders [1].

Given this explosion in human genome sequence data and the fact that many disease- associated mutations have never been identified previously, there is a pressing need for computational methods to prioritise genetic variants most likely to cause disease.
An often neglected factor that can influence mutation pathogenicity is the assembly of proteins into complexes [2,3]. For instance, when a multiple copies of a protein are present in the same complex, a heterozygous mutation can result in most assembled complexes containing a mixture of wildtype and mutated proteins. If a single mutated protein disrupts the function of the entire complex, this leads to a “dominant negative” mechanism, whereby a heterozygous mutation can cause an almost complete loss of function.
In this project, we will first perform a detailed large-scale structural bioinformatic investigation into the locations of human disease-associated mutations with respect to experimentally determined and homology modelled protein complex structures. We will use various computational methods to model the effects of each mutation on the stability of individual protein subunits and the assembly of protein complexes. This will allow us to identify protein structural features that can best discriminate between different types of mutations.

The second part of this project will involve developing a new computational predictor of mutation pathogenicity using machine-learning techniques. Rather than simply predicting the probability of a mutation being pathogenic, our new method will assign a relative probability that a mutation involves a dominant negative mechanism vs. a gain- or loss-of-function mechanism. This will be particularly useful for identifying new de novo variants, which are more likely to manifest their phenotypic effect via a dominant negative mechanism. We will apply this method to mutations identified by the DDD project to assess the practical benefits in terms of identifying causative mutations and predicting molecular mechanisms.

Application
This MRC DTP 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.

You can apply here via the University of Glasgow: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research-degrees/phds/precision-medicine