Efficient and Sensitive Mining System for G-Protein Coupled Receptors

描述（由申请人提供）： G蛋白偶联受体（GPCR）参与各种细胞信号传导过程，并由多种配体激活。许多重大疾病都涉及这些受体的功能障碍。因此，它们是药物干预的最重要的药物靶标之一。识别所谓的孤儿GPCR的功能和从基因组信息中搜索尚未发现的GPCR都可能导致新的GPCR药物发现。另一方面，GPCR序列是高度分化的，从不同的基因组中挖掘其成员蛋白质被证明是一个挑战。我们的长期目标是促进我们对GPCR之间功能差异机制的理解，同时提供有助于基础研究和GPCR药物开发的计算工具。我们在这项提案中的重点是开发一个高效和灵敏的蛋白质挖掘系统，专门针对GPCR序列进行优化。这项研究背后的具体假设是，GPCR的一级序列包含与其功能相关的足够信息，通过适当的方法，我们应该能够提取这些信息。在这项提案中，我们将开发和评估新的方法，可以有效地识别低序列相似性的GPCR（目标1）。我们的初步研究表明，与目前使用的基于相似性的方法相比，无相似性方法对远程和短相似性更敏感，这是从基因组数据中挖掘极其不同的蛋白质所需的质量。将这些不同的方法组合为多个过滤器，将开发一个分层挖掘系统（目标2）。我们的主要重点是通过集成多种方法来获得最佳采矿能力。数据库和网络接口系统提供了一个灵活和动态的工具，将促进我们自己的发展进程。该系统将向公众开放。我们还将对其他类型的蛋白质应用相同的策略，特别是包括核受体在内的多结构域蛋白质家族（Aim 3）。大多数真核生物蛋白质具有多个功能结构域。因此，将我们的蛋白质挖掘策略应用于这些蛋白质是开发适用于更广泛蛋白质的蛋白质分类系统的逻辑步骤。最后，我们将从不同的基因组中进行实际挖掘，包括未充分利用的短表达序列标签数据（目标4）。我们希望从不同的基因组中获得这些蛋白质家族的最全面的集合。

DESCRIPTION (provided by applicant): G-protein coupled receptors (GPCRs) are involved in various cellular signaling processes and activated by a diverse array of ligands. Many major diseases involve in malfunction of these receptors. Therefore, they are among the most important drug targets for pharmaceutical intervention. Identifying functions of so-called orphan GPCRs and searching not-yet-discovered GPCRs from genomic information both potentially lead to new GPCR drug discovery. On the other hand, GPCR sequences are highly diverged and mining their member proteins from diverse genomes turned out to be a challenge. Our long-term goal is to advance our understanding of the mechanisms of functional divergence among GPCRs, and at the same time to provide computational tools that will facilitate basic research and GPCR drug development. Our focus in this proposal is to develop an efficient and sensitive protein mining system specifically optimized for GPCR sequences. The specific hypothesis behind the proposed research is that the primary sequences of GPCRs contain sufficient information correlated to their functions, and with appropriate methods, we should be able to extract such information. In this proposal we will develop and evaluate new methods that can effectively identify GPCRs with low sequence similarities (Aim 1). Our preliminary study has shown that compared to currently used alignment-based methods, alignment-free methods are more sensitive to remote and short similarities, a desired quality for mining extremely divergent proteins from genomic data. Combining these various methods as multiple filters, a hierarchical mining system will be developed (Aim 2). Our primary focus is to gain the optimum mining power by integrating multiple methods. The database and web-interface system provides a flexible and dynamic tool that will facilitate our own development process. This system will be made available publicly. We will also apply the same strategy for other types of proteins, especially multi-domain protein families including nuclear receptors (Aim 3). The majority of eukaryotic proteins have multiple functional domains. Thus applying our protein mining strategy to these proteins is the logical step toward developing a protein classification system applicable for a wider array of proteins. Finally we will perform actual mining from diverse genomes including underutilized short Expressed Sequence Tags data (Aim 4). We expect to obtain the most comprehensive set of these protein families from various genomes.