Discovery of Protein Network Function

蛋白质网络功能的发现

• 批准号: 9199586
• 负责人: Tanja Kortemme
• 金额: $ 54.63万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9199586
• 关键词: Affect; Affinity; Amino Acid Sequence; Amino Acids; Animal Model; Binding; Biological; Biological Assay; Biological Process; Biomedical Engineering; Cell Cycle; Cell physiology; Collection; Complex; Computer Simulation; Data; Data Set; Development; Disease; Engineering; Gene Proteins; Genes; Genetic; Guanosine Triphosphate Phosphohydrolases; High-Throughput Nucleotide Sequencing; Human; Individual; Intervention; Knowledge; Lead; Link; Mammalian Cell; Mass Spectrum Analysis; Measurement; Methodology; Methods; Modeling; Molecular; Mutation; Organism; Phenotype; Physics; Point Mutation; Positioning Attribute; Process; Proteins; Quantitative Genetics; Research; Research Personnel; Resolution; Saccharomyces cerevisiae; Sequence Analysis; Severities; Signal Transduction; Signaling Protein; Structure; System; Techniques; Technology; Test Result; Testing; Therapeutic Intervention; Time; Translating; Translations; Work; Yeasts; base; design; experimental study; fitness; improved; in vitro Model; in vivo; innovation; insight; interdisciplinary approach; knock-down; multidisciplinary; mutant; new therapeutic target; novel therapeutics; outcome prediction; precision medicine; predictive modeling; protein complex; protein function; protein protein interaction; public health relevance; tool

 DESCRIPTION (provided by applicant): Large-scale biological datasets reveal increasingly complex genetic and protein-protein interaction networks. As a consequence of this complexity, for key proteins with many interaction partners that are found at central positions in the network, it is difficult to extract quantitative information on how each interaction contributes to distinctor overlapping cellular functions, and, importantly, how changes to individual interactions result in altered function or disease. This knowledge would be critical for progress in many fields, such as biological engineering that requires predictive control of signaling networks, or the development of new targeted interventions in precision medicine. The long-term objective of this project is to advance studies that dissect complex protein networks by creating and testing a new multidisciplinary bioengineering approach that links systematic computational prediction of molecular perturbations at the amino acid-level to their effects on biological processes at the systems-level. The project will initially target the highly-conserved multi-functional Gsp1/Ran GTPase that controls key eukaryotic processes. The approach first engineers defined perturbations to protein-protein interactions by amino acid mutations ("edge perturbations"). The second step determines the functional effects of these perturbations at the cellular and organism level. The project advances technologies developed in three groups and integrates them into a platform that combines physics-based modeling and reengineering of interactions (Kortemme), mechanistic analysis of sequence-structure-function-fitness relationships (Bolon), and large-scale physical and genetic interaction mapping (Krogan). Innovative aspects are (i) the new integration of approaches and (ii) preliminary data indicating that the approach can not only dissect existing Gsp1 functions but also discover new biological functions, even for this well-studied protein. Aim 1 proposes to develop, test, and advance a validated computational model that can be used both to engineer and to interpret quantitative perturbations to interactions in protein-protein networks. Aim 2 will test hypotheses from Aim 1 by determining the consequences of engineered perturbations on cellular function in the model organism S. cerevisiae, chosen for its genetic tractability and extensive available information to validate the approach. Integration of the model from Aim 1 and data from Aim 2 will lead to an improved model and new hypotheses that will in turn be tested, resulting in new knowledge of the mechanistic basis of systems-level changes in function. Aim 3 will translate our platform into mammalian cells, which will provide fundamental insights into conserved and divergent mechanisms of Gsp1/Ran that is 83% identical in amino acid sequence between yeast and human. Our study will result in a validated technological platform that can be applied to other systems to derive predictive models of consequences of mutations on cellular function and organismal fitness. Long-term, we expect this platform to impact bioengineering approaches as well as the development of new targeted therapies that make precise network perturbations.

 描述（由申请人提供）：大规模的生物数据集启用日益复杂的遗传和蛋白质相互作用，如这种复合物的结果 提取是关于每种相互作用如何有助于独特重叠的细胞功能的信息，重要的是，对单个相互作用的变化如何导致功能或疾病改变。为了剖析竞争和测试新的多学科生物工程的研究，将氨基酸级分子扰动的Sy Stemtate计算预测与系统级别的生物学过程相关。关键的真核过程。 - 相互作用（Kortemme）的低音模型和rengineerag，序列结构关系关系（BOLON）的机械分析（BOLON）E级物理和遗传相互作用映射（Krogan）是（Krogan） ii）初步数据表明该方法无法现有的GSP1功能，但还发现了新的生物学功能ELL研究的蛋白质1提出了开发，测试和推进验证的计算模型在模型有机体中设计的蛋白质蛋白网络。 AIM 2的模型的整合将导致改进的模型，而新的假设将以新的知识来对系统级别的机械基础进行测试。年度和人类之间的序列将导致经过验证的技术平台，可以应用于其他系统，以获取有关细胞功能和有机体适应性的突变模型。