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Prospecting for pH sensors in host and pathogen systems

探索宿主和病原体系统中的 pH 传感器

基本信息

批准号：
BB/V006592/1
负责人：
James Warwicker
金额：
$ 55.44万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FV006592%2F1
关键词：
Prospecting pH sensors host pathogen

项目摘要

Human cells contain several types of membrane-enclosed compartments. Such compartmentalisation gives metabolic advantages, isolating regions (organelles) and processes within the cytoplasm of a cell interior, for example genome organisation, energy generation, and protein degradation. Intracellular organelles also allow unequal distributions of small molecules and ions to be set up by pumps made of proteins residing in the membranes. Notably, gradients of pH (differences in the concentration of hydrogen ions) exist between several organelle interiors and the cytoplasm. Functioning of a cell depends on control of pH, as does its relationship, across the outer cell membrane, with the external environment. Two areas in which human health intersects with an understanding of pH are cancer growth and viral infection. Metabolic changes in tumour cells lead to alteration in the balance of pH between cell interior and exterior. Separately, many viruses (including SARS-CoV-2) make use of a low pH organelle to release their genome into a host cell. In both of these diseases modern techniques are being used for large-scale data acquisition. As a tumour grows, mutations accrue (called somatic mutations) that enable adaptation to the altered pH environment, and these are recorded (10,000s) and deposited in databases. Biochemical characterisation of SARS-CoV-2 is occurring at pace, including structural analysis with newly-developed techniques that are particularly suited to large assemblies such as viruses.Our work addresses the question of how to make a model that predicts the key elements of pH response in biology, i.e. molecular pH sensors. For viruses this would allow prediction of which viruses use low the pH infection route, where their pH sensors are located, and potentially lead to new antiviral strategies. In cancer adaptation to pH, somatic mutations are known, but which directly interact with pH-relevant pathways is mostly unknown. We aim to map mutation to atomic structure, and use the model to predict sites of pH sensing. Early work indicates that such a pipeline will reveal insights into metabolic changes in tumour growth, again with the potential to consider new therapeutic avenues. Predictions will be tested experimentally with a small number of exemplar systems.The method is a synthesis of mostly available tools for making structural models and predicting pH-dependence from structure. Modellers have access to a hierarchy of techniques, based on their level of detail and computational resource required. Our model will be based in the so-called coarse-grained part of this hierarchy, where the level of detail is scaled back to allow for calculations that are fast enough to provide a quick turnaround, essential for a web application. In this context, pH sensors are suited since they are focussed in a subset of amino acids in proteins. Our aim, through model development, benchmarking with known data, and testing in our own laboratory, is to enable any user, world-wide, to predict pH sensors in systems of interest to them. This approach will enable generation of testable hypotheses from the wealth of genomic and biochemical data being collected.

人类细胞包含几种类型的膜封闭区室。这种区室化提供了代谢优势，分离了细胞内部细胞质内的区域（细胞器）和过程，例如基因组组织，能量产生和蛋白质降解。细胞内的细胞器也允许小分子和离子的不均匀分布，这些小分子和离子被膜中的蛋白质泵所建立。值得注意的是，pH梯度（氢离子浓度的差异）存在于几个细胞器内部和细胞质之间。细胞的功能取决于pH值的控制，就像它的关系一样，穿过细胞外膜，与外部环境。人类健康与pH值的理解交叉的两个领域是癌症生长和病毒感染。肿瘤细胞中的代谢变化导致细胞内部和外部之间pH平衡的改变。另外，许多病毒（包括SARS-CoV-2）利用低pH值的细胞器将其基因组释放到宿主细胞中。在这两种疾病中，现代技术正在用于大规模数据采集。随着肿瘤的生长，突变（称为体细胞突变）增加，使适应改变的pH环境，这些被记录（10，000）并保存在数据库中。SARS-CoV-2的生物化学表征正在同步进行，包括使用特别适合于病毒等大型组装体的新开发技术进行结构分析。我们的工作解决了如何建立预测生物学中pH响应关键要素的模型的问题，即分子pH传感器。对于病毒，这将允许预测哪些病毒使用低pH感染途径，它们的pH传感器位于何处，并可能导致新的抗病毒策略。在癌症对pH的适应中，体细胞突变是已知的，但与pH相关通路直接相互作用的体细胞突变大多是未知的。我们的目标是将突变映射到原子结构，并使用该模型来预测pH传感的位点。早期的工作表明，这样的管道将揭示肿瘤生长中代谢变化的见解，再次有可能考虑新的治疗途径。预测将用少量的示例系统进行实验测试。该方法是一种合成的最有效的工具，用于建立结构模型和预测结构的pH依赖性。建模人员可以根据所需的详细程度和计算资源访问技术层次。我们的模型将基于这个层次结构中所谓的粗粒度部分，其中详细程度被缩减，以允许足够快的计算，以提供快速的周转，这对Web应用程序至关重要。在这种情况下，pH传感器是合适的，因为它们集中在蛋白质中的氨基酸子集中。我们的目标是，通过模型开发、已知数据的基准测试以及在我们自己的实验室中进行测试，使世界各地的任何用户都能够预测他们感兴趣的系统中的pH传感器。这种方法将能够从收集的基因组和生物化学数据的财富中产生可检验的假设。