Grant: $95,017 - National Institutes of Health - Jul. 28, 2009
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Award Description: 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.
Project Description: The parent grant has four specific aims designed to develop a system specifically optimized for mining G-protein coupled receptor (GPCR) proteins and to examine the applicability of the strategy developed for other families of proteins. The supplement was requested to specifically strengthen the Aims 2 and 3: Aim 2: Develop and implement a hierarchically integrated mining system optimized for GPCR discovery, and Aim 3: Develop and optimize a hierarchical mining system for the nuclear receptor related protein families. The main purpose of the requested supplement is to enhance the performance of the hierarchical classification, to improve 7TMRmine functionality, and to strengthen the Web server for its computing power. We have purchased four Linux computing nodes. These new nodes have been added to the PI Moriyama's existing Linux cluster (bioservx) to strengthen the Web server used for 7TMRmine. The current server is running on a two-Linux-node cluster using old 32bit computers. The new nodes are 64bit and have much faster CPU with much larger memory (12GB each). Once migration from the older to the new server is completed, we can remove size limitation for the submitted jobs. Many more users will be able to use the server more freely. An undergraduate student, Brandon Fulk (UNL), and a graduate student, Naeem Kanwal (UNO), have been supported by this supplement. They have been trained by the PI Moriyama and the Co-I Lu for mining GPCR sequences from a wide range of organisms, and for performing comparative genomics and molecular evolutionary analysis. In order to obtain confidence scores or supporting values for classification results, we have started analyzing predicted GPCR candidates using similarity search (BLASTP) against GPCRDB (the Information System for G Protein-Coupled Receptors; http://www.gpcr.org/7tm) database. Various similarity search analysis will be performed and the array of similarity scores will be assigned to each predicted candidates.
Jobs Summary: As an educational institution, jobs created or retained fall into broad categories of faculty salaries, administrative salaries, managerial professional salaries and clerical or technical salaries. They may also include some academic salaries for student workers. Salaries are used in support of research or other sponsored projects being performed at UNL. Faculty post-docs and graduate students are the primary recipients of salary dollars; however, some managerial or professional, clerical and technical or students may benefit as well. Faculty personnel usually include the titles professor, assistant professor, associate professor, instructor, assistant instructor and post-doctoral assistant. Administrative and clerical salaries are charged if they meet the criteria detailed in OMB Circular A-21. Keeping post-docs, graduate students and undergraduate students employed has an additional impact of allowing them to pursue additional education, preparing them for future employment. As a broader impact, results of some projects may result in additional jobs in the public sector as technology is expanded to that market. For this project and quarter, full-time equivalent positions were created and/or retained either by UNL or by sub-awards made from this grant, if applicable. Calculations were made in accordance with the Office of Management and Budget Memorandum M-09-21 and subsequent guidance as provided by the OMB. (Total jobs reported: 0)
Project Status: Less Than 50% Completed
This award's data was last updated on Jul. 28, 2009. Help expand these official descriptions using the wiki below.
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