Identification of Pathogenic Protein Mutations using Synthetic Biology, Structural Bioinformatics and Biochemistry

The aim of this project is to identify mutations which produce a pathogenic phenotype in ubiquitin signalling proteins using a technique called Deep Mutational Scanning (DMS). DMS integrates saturation mutagenesis with deep sequencing, allowing high throughput functional analysis of thousands of different mutant alleles in parallel. The focus will be on single amino-acid changes, but the potential epistatic effects of multiple mutations on a single protein will also be studied.
There are exponentially more multi-site mutants than single-site ones, so only a limited number will be covered by the DMS experiment. This data will be used as the basis for a machine-learning approach in order to train a computational predictor to make estimates of the phenotypic effect of the remaining epistatic interactions. The results will be compared to existing computational phenotype predictors, with particular focus on those mutations which were not identified by the DMS experiment.
Finally, a subset of mutants will be studied in detail by undertaking structural studies. The goal of this section is to try and understand why computational predictors of phenotypes are often inaccurate, and to gain further insight into how disruption of ubiquitin signalling leads to a disease state.
Ultimately a DMS dataset will be produced, which can help make predictions about ubiquitin signalling variants of unknown effect. The dataset will also be used to build a computational phenotype predictor and can be integrated with other datasets to train more general protein phenotype predictors.