Discovery of SAM domain functions

SAM域功能的发现

• 批准号: 8109311
• 负责人: Alejandro Daniel Meruelo
• 金额: $ 2.85万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8109311
• 关键词: Address; Adenylate Cyclase; Anterior; Binding; Binding Sites; Bioinformatics; Biological; Biological Assay; Biological Process; Cells; Disease; Drosophila genus; Drug Delivery Systems; Eukaryotic Cell; Goals; Hybrids; Individual; Lead; Learning; Luciferases; Malignant neoplasm of brain; Methodology; Modeling; Nucleic Acid Binding; Nucleic Acids; Oocytes; Oogenesis; Pattern; Polymers; Proteins; Proteomics; Publications; Regulation; Reporter; Repression; SAM Domain; Structure; Techniques; Tertiary Protein Structure; Testing; Yeasts; cell motility; gene repression; improved; insight; migration; mutant; polymerization; protein function; protein protein interaction

DESCRIPTION (provided by applicant): A salient feature of eukaryotic proteins is their modular construction. Proteins can gain new functionalities by incorporating new modules. The study of domain composition may suggest hypotheses regarding a protein function(s), and thus it has become desirable to define the function of individual protein domain modules. This is particularly important for SAM (sterile alpha motif) domains, which are among the most common protein modules found in eukaryotic cells. In contrast to many other well-characterized protein-protein interaction modules, SAM domains have considerably more diverse interaction modes. Under the hypothesis that learning module functions can provide biological insights, we propose to assign functions to thousands of uncharacterized SAM domains by a combination of bioinformatic and proteomic techniques. Aim 1: Expand our predictions of polymeric and non-polymeric SAM domains. Our first publication partly addressed Aim 1. We computationally identified those SAMs likely to be polymeric by calculating their interaction energy when threaded onto known polymeric structures. We found 694 likely polymers, including SAM domains from the proteins Lethal Malignant Brain Tumor, Bicaudal-C, Liprin-beta, Adenylate Cyclase, and Atherin. About half of all known SAM domains could not be evaluated, however, because of low homology to known SAM structures. As part of our continuing studies, we will expand the number of predictions by including a large number of recently determined structures. Aim 2: Experimentally validate and characterize predicted SAM polymers. We will first test whether the predicted polymers actually form polymers by examining the purified SAM domains by EM. We then will further investigate their function by creating mutants that block polymerization to see whether their known functions are interrupted. Aim 3: Assign functions to non-polymerizing SAM domains. A common alternative function is hetero- oligomerization with other SAM domains. Thus, for those SAMs that don't polymerize we will investigate whether they bind to one another instead, thereby developing a SAM domain interactome. This will be accomplished by means of yeast 2-hybrid screens. Another class of SAM domains bind to nucleic acids. We will identify these computationally, by looking for nucleic acid binding site features. The methodology outlined here to investigate global SAM domain functions is likely to be widely applicable to other domain types found in a variety of other proteins. Further, the discovery new SAM functions may help identify new drug targets for different diseases.

描述（由申请人提供）：真核蛋白的一个显著特征是它们的模块化结构。蛋白质可以通过加入新的模块来获得新的功能。结构域组成的研究可能会提出关于蛋白质功能的假设，因此定义单个蛋白质结构域模块的功能已成为可取的。这对于真核细胞中最常见的蛋白质模块SAM（无菌α基序）结构域尤其重要。与许多其他已被充分表征的蛋白质-蛋白质相互作用模块相比，SAM结构域具有相当多样化的相互作用模式。在学习模块功能可以提供生物学见解的假设下，我们建议通过生物信息学和蛋白质组学技术的结合，为数千个未表征的SAM结构域分配功能。目标1：扩展我们对聚合物和非聚合物SAM域的预测。我们的第一份出版物部分涉及目标1。我们通过计算它们在已知聚合物结构上的相互作用能，计算出这些sam可能是聚合物。我们发现了694种可能的聚合物，包括来自致命恶性脑肿瘤蛋白、Bicaudal-C、liprinβ、腺苷酸环化酶和动脉粥样硬化蛋白的SAM结构域。然而，由于与已知SAM结构的同源性较低，大约一半的已知SAM结构域无法评估。作为我们继续研究的一部分，我们将通过包括大量最近确定的结构来扩大预测的数量。目的2：实验验证和表征预测的SAM聚合物。我们将首先通过EM检查纯化的SAM结构域来测试预测的聚合物是否真的形成聚合物。然后我们将通过创建阻断聚合的突变体来进一步研究它们的功能，看看它们的已知功能是否被中断。目标3：将功能分配给非聚合的SAM域。一个常见的替代功能是与其他SAM域的异齐聚化。因此，对于那些不聚合的SAM，我们将研究它们是否相互结合，从而开发SAM域相互作用组。这将通过酵母双杂交筛选来完成。另一类SAM结构域与核酸结合。我们将通过寻找核酸结合位点的特征，通过计算来识别这些。本文概述的研究全局SAM结构域功能的方法可能广泛适用于其他各种蛋白质中发现的其他结构域类型。此外，发现新的SAM功能可能有助于确定不同疾病的新药物靶点。