Computational Genomics of Signal Transduction

描述(由申请人提供)： 信号转导的计算基因组学信号转导是一个对所有生物体都至关重要的普遍生物学过程。由于它们在疾病中的核心作用，我们自己固有的信号转导系统和细菌病原体的信号转导系统是药物设计的主要目标。我们的长期目标是了解细胞如何检测、传输和适应信号。这个项目的重点是细菌趋化系统，这是在分子水平上理解信号转导基本原理的最好的研究模型。该系统在复杂性和机制上都类似于高阶真核信号转导系统，是许多细菌病原体毒力的决定因素。趋化信号复合体由化学受体、适配蛋白和激酶组成。它得到了很好的保存，其整体结构组织的模型已经产生。然而，信号是如何在化学感受器内以及化学感受器(S)和激酶之间传递的还知之甚少。我们建议以我们的发现为基础，利用我们的工具开发专门解决化学感受器信号传递的潜在机制，并解开它们的多样性。我们将进行一些长的全原子分子动力学模拟，以测试锁定在信号开启和信号关闭状态的突变化学感受器的行为。这些突变体的生化、生物物理和行为特征将由我们的合作者进行。我们还将利用进化信息提炼高分辨率晶体结构中的化学感受器：适配器：激酶接口和接触部位。现有数据库中数以万计的化学受体序列不仅是潜在的药物靶标，而且是解决配体结合和蛋白质-蛋白质相互作用所需的进化信息的宝贵资源。然而，极端的序列变异和频繁的基因丢失和水平基因转移事件阻碍了它们的表征。我们的目标是通过对化学感受器、感觉和信号域进行全面的序列/结构分析来克服这些障碍。我们将对微生物化学感受器进行分类，并生产特定类别的高质量隐马尔可夫模型，使其能够在公共数据库中无缝识别。所有这些信息将被整合到微生物信号转导数据库MIST中，该数据库可以在线免费获得，新的结构域模型将提交给领先的蛋白质结构域数据库Pfam。目前的MIST能力将通过新的搜索和下载选项得到增强，并使用来自人类微生物组项目和所有其他当前元基因组数据集的大量序列进行更新，从而产生能够更好地为更大的科学界服务的资源。

DESCRIPTION (provided by applicant): Computational genomics of signal transduction Signal transduction is a universal biological process vital to all organisms. Due to their central role in disease, our own intrinsic signal transduction systems and those of bacterial pathogens are the primary targets of drug design. Our long-term goal is to understand how cells detect, transmit, and adapt to signals. The focus of this project is on the bacterial chemotaxis system, which is the best studied model for understanding fundamentals of signal transduction at the molecular level. This system resembles, both in complexity and mechanism, higher-order eukaryotic signal transduction systems and is a determinant of virulence in numerous bacterial pathogens. The chemotaxis signaling complex consists of chemoreceptors, an adaptor protein and a kinase. It is well-conserved and models for its overall structural organization have been produced. However, how signals are transmitted within a chemoreceptor and between chemoreceptor(s) and a kinase are poorly understood. We propose to build on our findings and capitalize on our tool development to specifically address the mechanisms underlying signaling by chemoreceptors and disentangle their diversity. We will perform a number of long all-atom molecular dynamics simulations to test the behavior of mutant chemoreceptors locked in signal-on and signal-off states. Biochemical, biophysical and behavioral characterization of these mutants will be carried out by our collaborators. We will also refine chemoreceptor:adaptor:kinase interfaces and contact sites in high-resolution crystal structures using evolutionary information. Tens of thousands of chemoreceptor sequences available in current databases are not only potential drug targets, but also an invaluable resource of evolutionary information needed to resolve ligand-binding and protein-protein interactions. However, extreme sequence variation and frequent events of gene loss and horizontal gene transfer impede their characterization. We aim at overcoming these barriers by carrying out comprehensive sequence/structure analyses of chemoreceptor sensory and signaling domains. We will classify microbial chemoreceptors and produce class-specific, high-quality hidden Markov models enabling their seamless identification in public databases. All this information will be integrated into MiST, a Microbial Signal Transduction database, which is freely available online and new domain models will be submitted to Pfam, the leading protein domain database. Current MiST capabilities will be enhanced with new search and download options and updated with a vast amount of sequences from the Human Microbiome Project and all other current metagenomics datasets, resulting in a resource that can better serve an even greater scientific community.