CAREER: Computational Analysis and Prediction of Genome-Wide Protein Targeting Signals and Localization

职业：全基因组蛋白质靶向信号和定位的计算分析和预测

• 批准号: 0845381
• 负责人: Jianjun Hu
• 金额: $ 57.98万
• 依托单位: University South Carolina Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845381&HistoricalAwards=false
• 关键词: CAREER; Computational; Analysis; Prediction; Genome

(This award is funded through the American Recovery and Reinvestment Act of 2009: Public Law 111-5). This is a CAREER award to support the research of Dr. Jianjun Hu, in the Department of Computer Science and Engineering at University of South Carolina. Dr. Hu is a second-year, tenure-track Assistant Professor.A typical cell has a size of only 10 microns while it contains about a billion proteins. How these proteins are transported from their synthesis sites to their target locations within or outside of the cell is still not well understood. Experiments showed that translocation of nascent proteins are usually guided by postal code-like targeting signals encoded within the amino acid sequences of proteins. Genome-wide identification and decoding of these so-called molecular zip codes are fundamental to comprehensive understanding of the cell. Experimentally identifying protein targeting signals is labor-intensive. Computational prediction of targeting signals is still a big challenge due to their low conservation at the amino acid level. Currently, no de novo discovery algorithm is available for identifying new protein targeting signals. Also missing are appropriate models and algorithms for comparing these signals. This grant is 1) investigating novel computational algorithms for de novo discovery of new protein targeting signals; 2) developing models and algorithms for representing, detecting, and comparing targeting signals and 3) developing a protein functional network-based integrative algorithms for protein localization prediction. A transformative result of these studies will be a sequence encoding scheme based on amino acid indexes. This scheme will convert protein sequences into sequences of amino acid groups (AAGs) such that conserved patterns can be represented, modeled and discovered. Finally, protein function networks will be derived from models of protein localization prediction. With this research, computational identification and decoding of genome-wide protein targeting signals and precise protein localization predication will greatly enhance the understanding of how proteins are assembled in a cell. Tools developed during this project will be made available on the lab website: http://mleg.cse.sc.eduAs a part of his CAREER grant, Dr. Hu will conduct short-term projects and student-run seminars to bring undergraduates into the bioinformatics research. A special effort will be made to change the perception that computer science is debugging code, as perceived by many high-school students. A novel computer game will be employed to show how bioinformatics addresses real-world problems. This will raise the public and especially the awareness and interest of K-12 students in bioinformatics. Students in the NSF STARTS Alliance program at the University of South Carolina will be targeted for students. Mini programming problems with a bioinformatics background will be developed for lower-level college students so that they will be exposed to bioinformatics early in their introductory programming courses. This project will also develop bioinformatics web services for de novo discovery, comparison, and retrieval of protein targeting signals and precise protein localization prediction.

(This该奖项通过2009年美国复苏和再投资法案资助：公法111-5）。这是一个职业奖，以支持博士胡建军的研究，在计算机科学与工程系在南卡罗来纳州。 胡博士是一位二年级的终身助理教授。一个典型的细胞只有10微米大小，但它含有大约10亿个蛋白质。这些蛋白质如何从它们的合成位点运输到它们在细胞内或细胞外的目标位置仍然没有很好的理解。实验表明，新生蛋白质的转运通常由蛋白质氨基酸序列中编码的邮政编码样靶向信号引导。全基因组识别和解码这些所谓的分子邮政编码是全面了解细胞的基础。实验识别蛋白质靶向信号是劳动密集型的。靶向信号的计算预测仍然是一个很大的挑战，因为它们在氨基酸水平上的保守性很低。目前，没有从头发现算法可用于识别新的蛋白质靶向信号。还缺少用于比较这些信号的适当模型和算法。该资助是1）研究新的计算算法，用于从头发现新的蛋白质靶向信号; 2）开发用于表示，检测和比较靶向信号的模型和算法; 3）开发基于蛋白质功能网络的整合算法用于蛋白质定位预测。这些研究的一个变革性结果将是基于氨基酸索引的序列编码方案。该方案将蛋白质序列转换为氨基酸组（AAGs）序列，从而可以表示，建模和发现保守模式。最后，蛋白质功能网络将从蛋白质定位预测模型中导出。通过这项研究，全基因组蛋白质靶向信号的计算识别和解码以及精确的蛋白质定位预测将大大提高对蛋白质如何在细胞中组装的理解。在这个项目中开发的工具将在实验室网站上提供：http://mleg.cse.sc.eduAs作为他职业生涯资助的一部分，胡博士将进行短期项目和学生举办的研讨会，使本科生参与生物信息学研究。一个特别的努力将作出改变的看法，计算机科学是调试代码，认为许多高中生。将采用一款新颖的电脑游戏来展示生物信息学如何解决现实世界的问题。这将提高公众，特别是K-12学生对生物信息学的认识和兴趣。南卡罗来纳州大学的NSF STARTS联盟项目的学生将成为学生的目标。将为低水平的大学生开发具有生物信息学背景的迷你编程问题，以便他们在入门编程课程的早期接触生物信息学。该项目还将开发生物信息学网络服务，用于从头发现，比较和检索蛋白质靶向信号和精确的蛋白质定位预测。