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)：大规模生物数据集揭示了日益复杂的遗传和蛋白质-蛋白质相互作用网络。作为这种复杂性的结果，对于在网络的中心位置发现的具有许多相互作用伙伴的关键蛋白质， 很难提取关于每个相互作用如何有助于区分重叠的细胞功能的定量信息，以及重要的是，个体相互作用的变化如何导致功能或疾病的改变。这些知识将对许多领域的进展至关重要，例如需要对信号网络进行预测性控制的生物工程，或者在精确医学中开发新的有针对性的干预措施。该项目的长期目标是通过创建和测试一种新的多学科生物工程方法来推进剖析复杂蛋白质网络的研究，该方法将氨基酸水平上的分子扰动的系统计算预测与它们在系统水平上对生物过程的影响联系起来。该项目最初将以高度保守的多功能Gsp1/RAN GTP酶为目标，该酶控制着关键的真核过程。该方法首先由工程师定义了氨基酸突变对蛋白质相互作用的扰动(“边缘扰动”)。第二步确定这些扰动在细胞和生物体水平上的功能影响。该项目推进了分三组开发的技术，并将它们整合到一个平台中，该平台结合了基于物理的相互作用建模和重组(Kortemme)、序列-结构-功能-适应关系的机械分析(Bolon)以及大规模物理和遗传相互作用图谱(Krogan)。创新的方面是(I)方法的新整合和(Ii)初步数据表明，该方法不仅可以剖析现有的Gsp1功能，而且可以发现新的生物学功能，即使对于这种研究得很好的蛋白质也是如此。目标1建议开发、测试和推进一个有效的计算模型，该模型可以用于设计和解释蛋白质-蛋白质网络中相互作用的定量扰动。目标2将通过确定工程扰动对模式生物酿酒酵母细胞功能的后果来测试目标1的假设，该模型生物因其遗传易感性和广泛的可用信息而被选中，以验证 接近。来自目标1的模型和来自目标2的数据的集成将导致改进的模型和新的假设，这些新的假设将反过来被检验，从而产生关于系统级功能变化的机制基础的新知识。Aim 3将我们的平台翻译成哺乳动物细胞，这将为Gsp1/RAN的保守和差异机制提供基本的见解，Gsp1/RAN在酵母和人类的氨基酸序列中有83%的同源性。我们的研究将产生一个经过验证的技术平台，可以应用于其他系统，以推导出突变对细胞功能和生物适应性的后果的预测模型。从长远来看，我们预计这个平台将影响生物工程方法以及产生精确网络扰动的新靶向疗法的开发。