Integrating computation and genetics to quantify specificity in protein networks

整合计算和遗传学来量化蛋白质网络的特异性

• 批准号: 8665442
• 负责人: Tanja Kortemme
• 金额: $ 40.98万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8665442
• 关键词: Actins; Address; Adverse effects; Affinity; Amino Acids; Animal Model; Back; Binding; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Biological Sciences; Biophysics; California; Cell physiology; Complex; Computational Science; Computer Analysis; Cytoskeleton; Data; Data Set; Development; Disease; Distant; Endocytosis; Engineering; Equilibrium; Evolution; Family; Family member; Fission Yeast; Future; Gene Deletion; Gene Dosage; Genes; Genetic; Genetic Structures; Genomics; Hybrids; In Vitro; Individual; Knowledge; Length; Link; Maps; Mass Spectrum Analysis; Mediating; Methods; Modeling; Molecular; Mutation; Nature; Organism; Pathway interactions; Pattern; Phage Display; Phase; Point Mutation; Process; Protein Array; Protein Family; Protein-Protein Interaction Map; Proteins; Publishing; Quantitative Genetics; Regulation; Research; Research Personnel; SH3 Domains; Saccharomyces cerevisiae; Saccharomycetales; San Francisco; Signal Transduction; Site; Specificity; Spottings; Structural Models; Structure; System; Technology; Tertiary Protein Structure; Testing; Therapeutic; Time; Universities; Validation; Western Blotting; Work; analytical tool; base; biological adaptation to stress; combinatorial; design; feeding; genetic analysis; genetic profiling; in vivo; insight; member; model design; mutant; novel; overexpression; physical science; preference; protein complex; protein expression; protein function; protein protein interaction; therapeutic development

DESCRIPTION (provided by applicant): Large-scale biological datasets (e.g. genetic and protein-protein interactions) are becoming easier to systematically produce in a variety of organisms, but it can be difficult to extract testable hypotheses on how individual proteins function. The overall objective of this research is to develop an experimental and computational platform that helps to address this gap between high-throughput information at the genomic scale and detailed mechanistic analysis of biological processes at the protein, protein domain and amino acid residue scale. To achieve this, the proposal integrates the complementary expertise of two investigators at the University of California-San Francisco in structural biophysics and computational protein modeling and design (Tanja Kortemme) and in large-scale, quantitative genetic and protein-protein interaction mapping strategies (Nevan Krogan). This work will specifically focus on specificity and promiscuity of protein recognition domains that mediate a considerable fraction of interactions in all biological processes. The central hypothesis this project will test is that there exist biologically important differences between the functional and biochemical overlap of members of a domain family. To test for such differences, we will simultaneously characterize the functional processes all members of a major domain family are involved in, and how these functions relate to the intrinsic protein recognition preferences of the family members. As a proof of principle, we aim to interrogate the family of 23 SH3 domain containing proteins in the model organism S. cerevisiae. SH3 domains have considerable biological importance: they are involved in a several critical processes in signal transduction, reorganization of the actin cytoskeleton, stress response and endocytosis. More practically, SH3 domains were selected as a manageable model system due to the amount of structural and biochemical data accumulated for this domain family. Aim 1 uses an unbiased large-scale genetic interaction mapping strategy to genetically interrogate SH3 domain deletions in all SH3-containing proteins in budding yeast so that their in vivo relevance can be studied. Aim 2 proposes to use this information, along with previously published physical interaction data, to aid in structure-based predictions of the recognition specificity of individual SH3 domains. Computational strategies using RosettaDesign will be used to reengineer domains to tune interaction specificity and promiscuity. These predictions will be tested in Aim 3 using biochemical, functional and genetic approaches and the resulting data will be used to refine the models generated in Aim 2. In the future, we intend to extend our findings and the experimental platform this project seeks to establish into other species, initially into fission yeast, but ultimately to higher organisms. We expect our developed framework to be broadly informative for applications in molecular reengineering as well as for development of therapeutics acting on interconnected protein networks.

描述(由申请人提供)：大规模生物数据集(例如，遗传和蛋白质-蛋白质相互作用)正变得更容易在各种生物体中系统地产生，但可能很难提取关于单个蛋白质如何功能的可测试假设。这项研究的总体目标是开发一个实验和计算平台，帮助解决基因组水平的高通量信息与蛋白质、蛋白质结构域和氨基酸残基水平的生物过程的详细机制分析之间的差距。为了实现这一目标，该提案整合了加州大学旧金山分校两名研究人员在结构生物物理学和计算蛋白质建模和设计(Tanja Kortemme)以及大规模定量遗传和蛋白质-蛋白质相互作用图谱绘制策略(Nevan Krogan)方面的互补专业知识。这项工作将特别关注蛋白质识别结构域的特异性和混杂性，这些结构域在所有生物过程中调节着相当大一部分的相互作用。这个项目将检验的中心假设是，在一个域家族的成员的功能和生化重叠之间存在生物学上的重要差异。为了测试这些差异，我们将同时表征一个主要结构域家族的所有成员都参与的功能过程，以及这些功能与家族成员固有的蛋白质识别偏好如何相关。作为原则的证明，我们的目标是询问模式生物酿酒酵母中包含蛋白质的23个SH3结构域的家族。SH3结构域具有重要的生物学意义：它们参与了信号转导、肌动蛋白细胞骨架重组、应激反应和内吞作用等几个关键过程。更实际的是，SH3结构域被选为可管理的模型系统，因为该结构域家族积累了大量的结构和生化数据。目的1利用一种无偏的大规模遗传作图策略，对芽期酵母中所有含SH3蛋白的SH3结构域缺失进行遗传学查询，以便研究它们在体内的相关性。Aim 2建议使用这些信息，以及之前发表的物理相互作用数据，以帮助基于结构的预测单个SH3结构域的识别特异性。使用RosettaDesign的计算策略将被用于重新设计域，以调整交互、专一性和混杂。这些预测将在目标3中使用生化、功能和遗传方法进行测试，所产生的数据将用于完善目标2中产生的模型。未来，我们打算将我们的发现和该项目试图建立的实验平台扩展到其他物种，最初是分裂酵母，但最终是高等生物。我们希望我们开发的框架将为分子重组以及作用于相互连接的蛋白质网络的治疗学的发展提供广泛的信息。