Mylvaganam Shankari E, Prabhakaran Muthuchidambram, Tudor Suresh S, Moezzi Saied, Ramnarayan Kalyanaraman
Structural Bioinformatics, San Diego, CA 92127, USA.
Biotechniques. 2002 Mar;Suppl:42-6.
Structural proteomics is an emerging paradigm that is gaining importance in the post-genomic era as a valuable discipline to process the protein target information being deciphered. The field plays a crucial role in assigning function to sequenced proteins, defining pathways in which the targets are involved, and understanding structure-function relationships of the protein targets. A key component of this research sector is accessing the three-dimensional structures of protein targets by both experimental and theoretical methods. This then leads to the question of how to store, retrieve, and manipulate vast amounts of sequence (1-D) and structural (3-D) information in a relational format so that extensive data analysis can be achieved. We at SBI have addressed both of these fundamental requirements of structural proteomics. We have developed an extensive collection of three-dimensional protein structures from sequence data and have implemented a relational architecture for data management. In this article we will discuss our approaches to structural proteomics and the tools that life science researchers can use in their discovery efforts.
结构蛋白质组学是一种新兴的模式，在后基因组时代正变得越来越重要，它作为一门有价值的学科来处理正在被破解的蛋白质靶标信息。该领域在为已测序蛋白质赋予功能、定义靶标所涉及的途径以及理解蛋白质靶标的结构-功能关系方面发挥着关键作用。这个研究领域的一个关键组成部分是通过实验和理论方法获取蛋白质靶标的三维结构。这就引出了一个问题，即如何以关系型格式存储、检索和处理大量的序列（一维）和结构（三维）信息，以便能够进行广泛的数据分析。我们SBI公司已经满足了结构蛋白质组学的这两个基本要求。我们从序列数据中开发了大量的三维蛋白质结构集合，并实现了用于数据管理的关系型架构。在本文中，我们将讨论我们在结构蛋白质组学方面的方法以及生命科学研究人员在其发现工作中可以使用的工具。
