Integrating computation and genetics to quantify specificity in 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）在各种生物体中变得越来越容易系统地产生，但是很难提取关于单个蛋白质如何功能的可测试假设。本研究的总体目标是开发一个实验和计算平台，以帮助解决基因组尺度上的高通量信息与蛋白质、蛋白质结构域和氨基酸残基尺度上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与设计（Tanja Kortemme）以及大规模、定量遗传和蛋白质-蛋白质相互作用制图策略（Nevan Krogan）方面的互补专业知识。这项工作将特别关注在所有生物过程中介导相当一部分相互作用的蛋白质识别结构域的特异性和混杂性。本项目将测试的中心假设是，在一个结构域家族成员的功能和生化重叠之间存在生物学上重要的差异。为了测试这种差异，我们将同时描述一个主要结构域家族的所有成员所参与的功能过程，以及这些功能如何与家族成员的内在蛋白质识别偏好相关。作为一种原理证明，我们的目标是询问模式生物酿酒酵母中含有23 SH3结构域的蛋白家族。SH3结构域具有相当大的生物学意义：它们参与信号转导、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，由于SH3结构域家族积累了大量的结构和生化数据，因此选择SH3结构域作为可管理的模型系统。Aim 1使用一种无偏的大规模遗传相互作用作图策略，对出芽酵母中所有含SH3蛋白的SH3结构域缺失进行遗传询问，以便研究它们在体内的相关性。Aim 2建议使用这些信息，以及先前发表的物理相互作用数据，来帮助基于结构的预测单个SH3结构域的识别特异性。使用RosettaDesign的计算策略将用于重新设计域以调整交互特异性和混杂性。这些预测将在Aim 3中使用生化，功能和遗传方法进行测试，结果数据将用于改进Aim 2中生成的模型。在未来，我们打算将我们的发现和实验平台扩展到其他物种，最初是裂变酵母，但最终是高等生物。我们期望我们开发的框架能够广泛地为分子重组应用以及作用于相互连接的蛋白质网络的治疗方法的开发提供信息。

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.