Elucidating the molecular determinants of dominant-negative mutations in protein complexes

随着人类基因组序列数据的爆炸式增长，以及许多疾病相关突变以前从未被观察到的事实，迫切需要可用于优先考虑最有可能致病的遗传变异的计算方法。在分子水平上影响突变的表型影响的一个经常被忽视的因素是蛋白质组装成同质复合物[1]。在这种复合体中，杂合突变可导致“显性负”效应，即单个突变蛋白破坏整个复合体的功能或“毒害”野生型蛋白[2]的活性。虽然这种显性负效应为遗传学家所熟知，并被认为是许多孟德尔遗传疾病和人类癌症的原因，但它从未被系统地研究过。该项目将结合计算和实验方法，以便：1)研究显性负突变的分子机制；2)建立模型来预测它们。首先，学生将对显性负突变进行系统的结构生物信息学分析，特别关注细胞骨架蛋白，其中有许多已知的例子和许多3D结构可用。他们将研究哪种类型的蛋白质最有可能与显性负机制相关，以及这与它们的组装模式和四级结构组织之间的关系。他们还将研究蛋白质复合物内错义突变的位置，并使用分子模型来预测突变对单个蛋白质亚基和复合物的影响。这将有助于评估显性负突变是否具有破坏性较小的总体趋势，并将有助于识别能够最好地区分不同类型突变的结构特征。这为学生提供了学习和发展生物信息学和蛋白质建模计算方法的机会。接下来，学生将通过实验表征从计算分析中确定的特定致病突变，重点关注微管蛋白和微管运动蛋白等候选物质，我们拥有优秀的试剂和专业知识。为了定义细胞骨架组织和动力学，细胞分裂和货物运输如何受到影响并导致疾病表型，学生将使用细胞生物学，超分辨率显微镜工具和定量分析。学生将剖析突变的潜在分子机制，使用单个微管和单个微管运动复合物的体外重建分析。这为学生提供了学习生化和细胞生物学方法的机会，并开发了新的超分辨率显微镜方法。最后，学生将评估目前可用的表型预测因子识别显性阴性突变的能力。然后，他们将使用机器学习方法将突变的分子特性与实验和疾病表型背后的机制见解结合起来，以开发用于预测显性负突变的通用和基因家族特定模型。

With the explosion in human genome sequence data and the fact that many disease-associated mutations have never been observed previously, there is a pressing need for computational methods that can be used to prioritise genetic variants that are most likely to be pathogenic. An often-neglected factor that can influence the phenotypic impact of a mutation at a molecular level is the assembly of proteins into homomeric complexes [1]. In such complex, a heterozygous mutation can result in a "dominant-negative" effect, in which a single mutated protein disrupts the function of the entire complex or "poisons" the activity of the wild-type protein [2]. While the dominant-negative effect is well known by geneticists, and is believed to be responsible for many Mendelian genetic disorders and cancers in humans, it has never been systematically studied. This project will combine computational and experimental approaches in order to: 1) investigate the molecular mechanisms underlying dominant-negative mutations; and 2) develop models to predict them.First, the student will perform systematic structural bioinformatics analyses of dominant-negative mutations, focusing in particular on cytoskeletal proteins, for which many examples are known and many 3D structures are available. They will investigate which types of proteins are most likely to be associated with a dominant-negative mechanism, and how this is related to their patterns of assembly and quaternary structure organisation. They will also study the locations of missense mutations within protein complexes, and use molecular modelling to predict the effects of mutations on individual protein subunits and complexes. This will allow the assessment of whether there is a general tendency for dominant-negative mutations to be less disruptive, and will facilitate the identification of structural features that can best discriminate between different types of mutations.This offers the student the opportunity to learn and develop computational methodology in bioinformatics and protein modelling.Next, the student will experimentally characterise specific pathogenic mutations identified from the computational analyses, focusing on candidates such as tubulin and microtubule motor proteins for which we have excellent reagents and expertise available3. To define how the cytoskeletal organization and dynamics, cell division and cargo transport are affected and result in the disease phenotype, the student will use cell biology, super-resolution microscopy tools, and quantitative analysis. The student will dissect the underlying molecular mechanism of the mutants, using in vitro reconstitution assays of individual microtubules and single microtubule motor complexes.This offers the student the opportunity to learn biochemical and cell biology approaches and develop novel super-resolution microscopy methods.Finally, the student will assess the ability of currently available phenotype predictors to identify dominant-negative mutations. They will then use machine-learning approaches to integrate the molecular properties of mutations with the mechanistic insights underlying the experimental and disease phenotypes, in order to develop general and gene-family specific models for predicting dominant-negative mutations.