Evolutionary and thermodynamical features of musculoskeletal disease mutations is human intrinsically disordered protein regions

肌肉骨骼疾病突变的进化和热力学特征是人类本质上无序的蛋白质区域

• 批准号: 2114913
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2114913
• 关键词: Evolutionary; thermodynamical; features; musculoskeletal; disease

There are proteins that are well characterized with regard to their three dimensional structure. In particular it is known that parts of them act as "functional" units, e.g. active and binding sites or functional domains. However, within proteins, regions of intrinsically disorder occur and these are characterized by a lack of a well-defined three-dimensional structure. Although these disordered regions do not show a particular higher conformational state, they are known to be functionally important, such as through their involvement in protein-protein interactions or DNA/RNA binding. In a recent work we have shown that there is ongoing positive selection that contributes substantially to the evolution of human long intrinsically disordered protein regions. Furthermore, these protein regions are enriched in posttranslational modification sites as well as regions and motifs (annotated sequence stretches of biological importance), but surprisingly disease mutations tend to occur much less frequently in disordered regions (Uversky et al., 2014), with the exception of mutations associated with musculoskeletal diseases. This timely project aims to understand why disease mutations tend to be less frequent in disordered protein regions. The focus of this project will lie on the exceptional group of mutations involved in musculoskeletal diseases that are enriched in disordered protein regions by comparing them to those involved in other disease groups. Fundamental will be a novel genomic comparative approach currently developed in the Gossmann lab targeted at the identification of the evolutionary properties of disease-associated sites. Furthermore, in collaboration with Daniel Rigden from the University of Liverpool, we will conduct molecular dynamic simulations to investigate three dimensional features of disease associated mutations on protein flexibility and protein-ligand interactions. Furthermore we will exploit machine learning approaches to predict protein disorder on the single site residue effects. Experimental evidence and the underlying mechanistics of disease candidate sites can then functionally be tested in a fly model in collaboration with the Mirre Simons lab at the University of Sheffield. This will ultimately gain insights into whether disease mutations are genuinely less likely to occur in disordered protein regions or whether our lacking understanding of disease properties associated with disordered protein regions has led to an under-annotation in the respective databases. For this highly innovative, collaborative and interdisciplinary PhD the respective candidate should have a strong background in biology, molecular biology and genetics as well basic programming knowledge or at least a strong interest in computational approaches to investigate fundamental biological problems. A background in bioinformatics is of advantage, however all necessary approaches will be taught during the duration of the PhD. This project will take advantage of multiple biological "big" data sets, such as the 1000-Genome project, Uniprot, large-scale mammalian phylogenies and their respective whole genome information, as well as PDB and several secondary databases.

有一些蛋白质在其三维结构方面得到了很好的表征。特别地，已知它们的部分充当“功能”单元，例如活性和结合位点或功能结构域。然而，在蛋白质中，存在固有的无序区域，这些区域的特征在于缺乏明确的三维结构。尽管这些无序区域没有显示出特定的更高构象状态，但已知它们在功能上是重要的，例如通过它们参与蛋白质-蛋白质相互作用或DNA/RNA结合。在最近的一项工作中，我们已经表明，有正在进行的积极选择，大大有助于人类长内在无序蛋白质区域的进化。此外，这些蛋白质区域富含翻译后修饰位点以及区域和基序（具有生物学重要性的注释序列段），但令人惊讶的是，疾病突变倾向于在无序区域中发生频率低得多（Uversky等人，2014），与肌肉骨骼疾病相关的突变除外。这个及时的项目旨在了解为什么疾病突变在无序蛋白质区域中往往不那么频繁。该项目的重点将在于肌肉骨骼疾病中涉及的特殊突变组，通过将其与其他疾病组中涉及的突变进行比较，这些突变在无序蛋白区域中富集。基本将是一种新的基因组比较方法，目前在Gossmann实验室开发的目标是识别疾病相关位点的进化特性。此外，我们将与利物浦大学的丹尼尔里格登合作，进行分子动力学模拟，以研究蛋白质柔性和蛋白质-配体相互作用的疾病相关突变的三维特征。此外，我们将利用机器学习的方法来预测蛋白质的单位点残基效应的障碍。实验证据和潜在的机制，疾病候选网站，然后可以在功能上进行测试，在苍蝇模型与米尔西蒙斯实验室在谢菲尔德大学。这将最终深入了解疾病突变是否真的不太可能发生在无序蛋白质区域，或者我们是否缺乏对与无序蛋白质区域相关的疾病特性的理解，导致相应数据库中的注释不足。对于这个高度创新，协作和跨学科的博士学位，相应的候选人应该在生物学，分子生物学和遗传学以及基本的编程知识方面有很强的背景，或者至少对研究基本生物学问题的计算方法有浓厚的兴趣。生物信息学的背景是有利的，但所有必要的方法将在博士期间教授。这一项目将利用多个生物“大”数据集，如千人基因组项目、Uniprot、大规模哺乳动物基因组及其各自的全基因组信息，以及PDB和若干二级数据库。