Prospecting for pH sensors in host and pathogen systems

人体细胞包含几种类型的膜封闭的隔室。这种区隔化提供了代谢优势，隔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。胞内细胞器也允许小分子和离子的不均匀分布，由驻留在膜上的蛋白质组成的泵来建立。值得注意的是，在几个细胞器内部和细胞质之间存在pH梯度（氢离子浓度的差异）。细胞的功能取决于对pH值的控制，就像它与外部环境的关系一样，通过外细胞膜。人类健康与pH值相互交叉的两个领域是癌症生长和病毒感染。肿瘤细胞的代谢变化导致细胞内外pH平衡的改变。另外，许多病毒（包括SARS-CoV-2）利用低pH值细胞器将其基因组释放到宿主细胞中。在这两种疾病中，正在使用现代技术进行大规模数据采集。随着肿瘤的生长，突变（称为体细胞突变）产生，使其能够适应改变的pH环境，这些突变被记录下来（10,000次）并存入数据库。SARS-CoV-2的生化特征正在快速进行，包括使用新开发的技术进行结构分析，这些技术特别适合于病毒等大型组装体。我们的工作解决了如何建立一个模型来预测生物学中pH反应的关键要素的问题，即分子pH传感器。对于病毒来说，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相关途径直接相互作用的突变大多是未知的。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感应位点。早期的工作表明，这样的管道将揭示对肿瘤生长代谢变化的见解，再次具有考虑新的治疗途径的潜力。预测将用少量的范例系统进行实验测试。该方法综合了大多数可用的工具，用于制作结构模型和预测ph依赖于结构。建模人员可以访问基于其详细程度和所需计算资源的技术层次结构。我们的模型将基于这个层次结构中所谓的粗粒度部分，其中细节级别被缩减，以允许足够快的计算来提供快速周转，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。我们的目标是，通过模型开发，对已知数据进行基准测试，并在我们自己的实验室进行测试，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丰富的基因组和生化数据中产生可测试的假设。

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.