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性增长，以及许多与疾病相关的突变以前从未被观察到的事实，迫切需要一种计算方法，可以用来区分最有可能致病的基因变异的优先顺序。在分子水平上影响突变表型影响的一个经常被忽视的因素是蛋白质组装成同源复合体[1]。在这种复合体中，杂合突变可能导致“显性-负性”效应，即单个突变的蛋白质扰乱整个复合体的功能或“毒害”野生型蛋白质的活性[2]。虽然显性负效应是遗传学家所熟知的，并被认为是导致人类许多孟德尔遗传疾病和癌症的原因，但它从未被系统研究过。这个项目将结合计算和实验方法，以：1)研究显性-负性突变的分子机制；2)开发模型来预测它们。首先，学生将对显性-负性突变进行系统的结构生物信息学分析，特别是细胞骨架蛋白质，其中许多例子是已知的，许多3D结构是可用的。他们将研究哪些类型的蛋白质最有可能与显性-负性机制相关，以及这与它们的组装模式和第四级结构组织方式有何关系。他们还将研究错义突变在蛋白质复合体中的位置，并使用分子模型来预测突变对单个蛋白质亚单位和复合体的影响。这将允许评估是否存在显性-负性突变破坏性较小的一般趋势，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开发生物信息学和蛋白质建模中的计算方法的机会。接下来，学生将通过实验表征从计算分析中识别的特定致病突变，重点是我们拥有优秀试剂和专业知识的候选微管和微管马达蛋白3。为了确定细胞骨架的组织和动力学、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。学生将使用单个微管和单个微管马达复合体的体外重建分析来剖析突变的潜在分子机制。这为学生提供了学习生化和细胞生物学方法并开发新的超分辨率显微镜方法的机会。最后，学生将评估目前可用的表型预测因子识别显性-负性突变的能力。然后，他们将使用机器学习方法，将突变的分子属性与实验和疾病表型背后的机制洞察力相结合，以开发预测显性-负性突变的一般模型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.