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

该项目的目的是使用一种名为深度突变扫描(DMS)的技术来识别在泛素信号蛋白中产生致病表型的突变。DMS将饱和突变与深度测序相结合，允许并行进行数千个不同突变等位基因的高通量功能分析。重点将放在单一氨基酸的变化上，但也将研究多个突变对单一蛋白质的潜在上位效应。多点突变的数量比单点突变要多得多，因此DMS实验只涵盖有限数量的突变。这些数据将被用作机器学习方法的基础，以便训练计算预测者，以估计剩余上位性相互作用的表型效应。结果将与现有的计算表型预测因子进行比较，特别是那些没有被DMS实验识别的突变。最后，将通过结构研究来详细研究突变的子集。这一部分的目标是尝试和理解为什么表型的计算预测通常是不准确的，并进一步了解泛素信号是如何中断导致疾病状态的。最终将产生一个DMS数据集，它可以帮助预测未知效果的泛素信号变体。该数据集还将用于构建计算表型预测器，并可与其他数据集集成以训练更通用的蛋白质表型预测器。

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.