Computational Genomics of Signal Transduction

信号转导的计算基因组学

• 批准号: 10393584
• 负责人: Igor B. Jouline
• 金额: $ 38.17万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10393584
• 关键词: Address; Bacteria; Bacterial Infections; Biological Process; Cells; Chemicals; Chemoreceptors; Chemotaxis; Collaborations; Communities; Data Set; Databases; Disease; Drug Design; Drug Targeting; Genomics; Goals; Health; Human; Human Microbiome; Knowledge; Laboratories; Ligands; Malignant Neoplasms; Metagenomics; Missense Mutation; Modeling; Molecular; Organism; Phosphotransferases; Process; Resources; Role; Signal Transduction; Signal Transduction Pathway; Specificity; Study models; System; Update; Virulence; comparative genomics; developmental disease; microbial; pathogenic bacteria; predictive modeling; public database; receptor; small molecule; tool development

PROJECT SUMMARY Signal transduction is a universal biological process vital to all organisms. Due to their central role in disease, signal transduction systems in humans and in 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 main 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 and also an important determinant of virulence in many pathogenic bacteria. The main unanswered questions that we propose to address are: (1) which small molecule ligands are recognized by bacteria, (2) how these signals are transmitted from chemoreceptors to a dedicated kinase, and 3) what are the determinants of receptor/kinase specificity. We propose to build on our previous findings and capitalize on our tool development to obtain this knowledge in collaboration with several experimental laboratories and to capture this knowledge as predictive models. These models will be stored in public databases, such as Pfam and our own MiST (Microbial Signal Transduction) 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 other metagenomics datasets, resulting in a resource that can better serve an even greater scientific community. Finally, we will extend our evolutionary analyses to the key eukaryotic signal transduction pathway, where we aim at predicting consequences of missense mutations in the context of developmental disorders and cancer.

项目摘要 信号转导是一个普遍的生物过程，对所有生物体都至关重要。由于它们在疾病中的核心作用， 人类和细菌病原体中的信号转导系统是药物设计的主要目标。我们 长期目标是了解细胞如何检测，传输和适应信号。该项目的主要重点是 在细菌趋化系统，这是最好的研究模型，了解基本的信号 在分子水平上的转导，也是许多病原菌毒力的重要决定因素。 我们提出要解决的主要问题是：（1）哪些小分子配体是 （2）这些信号如何从化学感受器传递到专用激酶，以及 3)受体/激酶特异性的决定因素是什么。我们建议在我们以前的研究结果的基础上， 利用我们的工具开发，与几个实验性的合作来获得这些知识。 实验室，并捕捉这些知识作为预测模型。这些模型将被公开存放 数据库，如Pfam和我们自己的MiST（微生物信号转导）数据库。当前MiST功能 将通过新的搜索和下载选项进行增强，并通过大量的序列进行更新。 人类微生物组计划和其他宏基因组学数据集，产生了一种可以更好地为人类服务的资源。 更大的科学界。最后，我们将把我们的进化分析扩展到关键的真核信号 转导途径，在那里我们的目标是预测的背景下错义突变的后果， 发育障碍和癌症。