McMullan Daniel, Canaves Jaume M, Quijano Kevin, Abdubek Polat, Nigoghossian Edward, Haugen Justin, Klock Heath E, Vincent Juli, Hale Joanna, Paulsen Jessica, Lesley Scott A
Joint Center for Structural Genomics, Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121-1127, USA.
J Struct Funct Genomics. 2005;6(2-3):135-41. doi: 10.1007/s10969-005-2898-1.
The production of large numbers of highly purified proteins for X-ray crystallography is a significant bottleneck in structural genomics. At the Joint Center for Structural Genomics (JCSG; http://www.jcsg.org), specific automated protein expression, purification, and analytical methods are being utilized to study the proteome of Thermotoga maritima. Anion exchange and size exclusion chromatography (SEC), intended for the production of highly purified proteins, have been automated and the procedures are described here in detail. Analytical SEC has been included as a standard quality control test. A biological unit (BU) is the macromolecule that has been proven or is presumed to be functional. Correct assignment of BUs from protein structures can be difficult. BU predictions obtained via the Protein Quaternary Structure file server (PQS; http://pqs.ebi.ac.uk/) were compared to SEC data for 16 representative T. maritima proteins whose structures were solved at the JCSG, revealing an inconsistency in five cases. Herein, we report that SEC can be used to validate or disprove PQS-derived oligomeric models. A substantial amount of associated SEC and structural data should enable us to use certain PQS parameters to gauge the accuracy of these computational models and to generally improve their predictions.
为X射线晶体学生产大量高度纯化的蛋白质是结构基因组学中的一个重大瓶颈。在结构基因组学联合中心(JCSG;http://www.jcsg.org),正在利用特定的自动化蛋白质表达、纯化和分析方法来研究海栖热袍菌的蛋白质组。用于生产高度纯化蛋白质的阴离子交换和尺寸排阻色谱法(SEC)已实现自动化,本文将详细描述这些程序。分析型SEC已被纳入标准质量控制测试。生物单位(BU)是已被证明或被假定具有功能的大分子。从蛋白质结构正确分配BU可能很困难。将通过蛋白质四级结构文件服务器(PQS;http://pqs.ebi.ac.uk/)获得的BU预测结果与16种在JCSG解析了结构的代表性海栖热袍菌蛋白质的SEC数据进行比较,发现有5例不一致。在此,我们报告SEC可用于验证或反驳源自PQS的寡聚体模型。大量相关的SEC和结构数据应能使我们利用某些PQS参数来评估这些计算模型的准确性,并总体上改进它们的预测。