Efficient and Sensitive Mining System for G-Protein Coupled Receptors

G 蛋白偶联受体高效灵敏的挖掘系统

基本信息

批准号：
7885750
负责人：
ETSUKO MORIYAMA
金额：
$ 9.5万
依托单位：
UNIVERSITY OF NEBRASKA LINCOLN
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2010-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7885750
关键词：
Accounting Amino Acid Sequence Amino Acids Animal Model Anxiety Arabidopsis Basic Science Bioinformatics Biological Assay Biophysics Cells Classification Code Collaborations Communities Computing Methodologies Core Facility Crystallography Data Databases Descriptor Development Disadvantaged Disease Drug Delivery Systems Expressed Sequence Tags Faculty Failure Functional disorder G-Protein-Coupled Receptors GTP-Binding Proteins Genes Genome Genomics Goals Hand Homology Modeling Inflammation Integral Membrane Protein Intervention Investigation Lead Left Letters Ligands Marketing Methods Mining Modeling Molecular Molecular Biology Molecular Evolution Nuclear Receptors Obesity Outcome Pain Pattern Recognition Pharmaceutical Preparations Pharmacologic Substance Plants Positioning Attribute Process Property Protein Array Protein Family Proteins Research Research Personnel Resources Rhodopsin Sequence Analysis Set protein Specificity Structure System Testing Time Training Transmembrane Domain Uncertainty Work base body system cell type comparative computerized data processing computerized tools data mining depression design drug development drug discovery experience flexibility high throughput screening human GPRC5C protein hypertensive heart disease innovation interest member prototype receptor statistics structural biology tool user-friendly web interface

项目摘要

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.

描述（由申请人提供）： G蛋白偶联受体（GPCR）参与了各种细胞信号传导过程，并被多种配体阵列激活。许多主要疾病涉及这些受体的故障。因此，它们是药物干预的最重要的药物靶标之一。从基因组信息中识别所谓的孤儿GPCR的功能，并从尚未发现的GPCR搜索均可能导致新的GPCR药物发现。另一方面，GPCR序列具有很高的分歧，并从不同的基因组中挖掘了其成员蛋白是一个挑战。我们的长期目标是提高我们对GPCR功能差异机制的理解，同时提供将促进基础研究和GPCR药物开发的计算工具。我们在该建议上的重点是开发一种针对GPCR序列专门优化的有效敏感蛋白质挖掘系统。拟议的研究背后的具体假设是，GPCR的主要序列包含足够的信息与其功能相关，并且使用适当的方法，我们应该能够提取此类信息。在此提案中，我们将开发和评估可以有效识别具有低序列相似性的GPCR的新方法（AIM 1）。我们的初步研究表明，与当前使用的基于对齐的方法相比，无对齐的方法对远程和短期相似性更敏感，这是开采与基因组数据极为不同的蛋白质的期望质量。将这些各种方法与多个过滤器相结合，将开发一个分层采矿系统（AIM 2）。我们的主要重点是通过整合多种方法来获得最佳的采矿能力。数据库和Web-Interface系统提供了一种灵活而动态的工具，可以促进我们自己的开发过程。该系统将公开提供。我们还将为其他类型的蛋白质（尤其是包括核受体的多域蛋白质家族）应用相同的策略（AIM 3）。大多数真核蛋白具有多个功能域。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采矿，包括未充分利用的短表达序列标签数据（AIM 4）。我们希望从各种基因组中获得最全面的这些蛋白质家族。