A Computational Capability for Fast and Reliable Characterization of Protein Complexes

快速可靠地表征蛋白质复合物的计算能力

• 批准号: 0213840
• 负责人: Ying Xu
• 金额: $ 88.86万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2003-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0213840&HistoricalAwards=false
• 关键词: Computational; Capability; Fast; Reliable; Characterization

Protein-protein interactions are at the heart of biological activities. They constitute the basic components of many biological processes such as signal transduction, cell-cycle control, metabolism, and general cellular machines. Characterization of protein-protein interactions in protein complexes in a cell, in a systematic manner, represents a highly challenging and important problem to functional genomics and proteomics in the post-genome sequencing era. An integrated computational capability for characterization of protein complexes in a cell will be developed, through (a) analyzing mass spectrometry data and chemical cross-linking information and (b) protein docking prediction under geometric constraints derived from these experimental data. Initially this capability will be tested and validated on a selected set of protein complexes from yeast, as a proof of principle. When fully developed, this capability will be used for genome-scale cataloging of protein complexes in yeast (and other genomes in general). The specific aims of this proposed project are: (i) development of improved computational methods for locating and identifying cross-links, particularly inter-molecular ones, from mass spectrometry data; (ii) development of new computational methods for identification of protein complexes and their component proteins, through analysis of cross-linking and other experimental data; (iii) development of new computational methods for data-constrained docking of two proteins that will significantly improve the existing docking methods in both prediction accuracy and application generality; (iv) development of new computational methods for data-constrained multi-party protein docking; and (v) applications of the developed methods to a selected set of protein complexes for their complex structure characterization. The proposed computational capability will significantly improve the ability to interrogate protein-protein interactions in a way not possible before, and open new doors for the emerging fields of functional genomics and proteomics.Investigation of these computational capabilities for characterization of protein complexes will provide a number of opportunities for graduate students and postdoctoral trainees who are interested in moving into the emerging field of computational biology/bioinformatics from other disciplines. A number of postdoctoral trainees will be hired to implement some of the key components of this research, and Ph.D. students will be recruited to work on this project as part of their thesis projects. In addition, a computational biology course will be offered to the graduate students of the joint ORNL/UTK Graduate Program in Genome Science and Technology, in the Fall semester of 2002. Some well-defined but challenging issues encountered in this project will be used as student term projects through this course. The challenging scientific problems to be solved in this project will expose the graduate students and postdoctoral trainees to the core issues of bioinformatics, i.e., intelligent and meaningful interpretation of massive amount of biological data and computational modeling of biological structures/processes, providing an excellent opportunity for hands-on training. New scientific findings through the implementation of this project will be made publicly accessible through journal publications and conference presentations. Computer software to be developed in this project will be made freely available to the academic and government organizations through the Internet. All the biological data generated in this project will be organized as databases, and also be made freely and publicly available through the Internet.

蛋白质之间的相互作用是生物活动的核心。它们构成了许多生物过程的基本组成部分，如信号转导、细胞周期控制、代谢和一般细胞机器。以系统的方式表征细胞中蛋白质复合物中的蛋白质-蛋白质相互作用，是后基因组测序时代功能基因组学和蛋白质组学的一个极具挑战性和重要的问题。通过(a)分析质谱数据和化学交联信息，以及(b)根据这些实验数据得出的几何约束下的蛋白质对接预测，将开发细胞中蛋白质复合物表征的综合计算能力。最初，这种能力将在酵母中选定的一组蛋白质复合物上进行测试和验证，作为原理的证明。当完全开发时，这种能力将用于酵母（以及其他基因组）中蛋白质复合物的基因组级编目。该项目的具体目标是：(i)开发改进的计算方法，用于从质谱数据中定位和识别交联，特别是分子间交联；（ii）通过分析交联和其他实验数据，开发鉴定蛋白质复合物及其组成蛋白的新计算方法；（iii）开发数据约束下两种蛋白质对接的新计算方法，显著提高现有对接方法的预测精度和应用普遍性；（iv）数据约束下多方蛋白对接计算新方法的发展；(v)将开发的方法应用于选定的一组蛋白质复合物的复杂结构表征。所提出的计算能力将以一种前所未有的方式显著提高询问蛋白质-蛋白质相互作用的能力，并为功能基因组学和蛋白质组学的新兴领域打开新的大门。研究这些蛋白质复合物表征的计算能力将为研究生和博士后学员提供许多机会，他们有兴趣从其他学科进入计算生物学/生物信息学的新兴领域。将聘请一批博士后实习生来实施本研究的一些关键组成部分，并将招募博士生作为其论文项目的一部分来研究本项目。此外，将于2002年秋季学期为ORNL/UTK基因组科学与技术联合研究生项目的研究生开设一门计算生物学课程。本专题中遇到的一些明确但具有挑战性的问题将作为本课程的学生学期专题。本项目所要解决的具有挑战性的科学问题将使研究生和博士后学员接触到生物信息学的核心问题，即对大量生物数据的智能和有意义的解释以及生物结构/过程的计算建模，为实践培训提供了极好的机会。通过实施这个项目，新的科学发现将通过期刊出版物和会议报告向公众开放。在这个项目中开发的计算机软件将通过互联网免费提供给学术和政府机构。该项目产生的所有生物数据将被组织成数据库，并通过互联网免费公开提供。