Discovery of SAM domain functions

SAM域功能的发现

• 批准号: 7899827
• 负责人: Alejandro Daniel Meruelo
• 金额: $ 3.08万
• 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 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7899827
• 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）：真核蛋白质的一个显着特征是它们的模块化结构。蛋白质可以通过整合新模块来获得新功能。结构域组成的研究可能提出有关蛋白质功能的假设，因此需要定义各个蛋白质结构域模块的功能。这对于 SAM（无菌 α 基序）结构域尤其重要，SAM 结构域是真核细胞中最常见的蛋白质模块之一。与许多其他充分表征的蛋白质-蛋白质相互作用模块相比，SAM 结构域具有更加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将功能分配给数千个未表征的 SAM 域。目标 1：扩展我们对聚合和非聚合 SAM 结构域的预测。我们的第一篇出版物部分解决了目标 1。我们通过计算 SAM 穿入已知聚合物结构时的相互作用能，通过计算确定了那些可能是聚合物的 SAM。我们发现了 694 种可能的聚合物，包括来自致死性恶性脑肿瘤、Bicaudal-C、Liprin-beta、腺苷酸环化酶和 Atherin 蛋白质的 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。作为我们持续研究的一部分，我们将通过纳入大量最近确定的结构来扩大预测的数量。目标 2：通过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正形成聚合物。然后，我们将通过创建阻止聚合的突变体来进一步研究它们的功能，以观察它们的已知功能是否被中断。目标 3：将功能分配给非聚合 SAM 域。常见的替代功能是与其他 SAM 结构域的异源寡聚化。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 结构域相互作用组。这将通过酵母 2 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特征来通过计算来识别这些。这里概述的研究全局 SAM 结构域功能的方法可能广泛适用于各种其他蛋白质中发现的其他结构域类型。此外，新的 SAM 功能的发现可能有助于确定针对不同疾病的新药物靶点。