Yu Nancy Y, Laird Matthew R, Spencer Cory, Brinkman Fiona S L
Department of Molecular Biology & Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Nucleic Acids Res. 2011 Jan;39(Database issue):D241-4. doi: 10.1093/nar/gkq1093. Epub 2010 Nov 10.
The subcellular localization (SCL) of a microbial protein provides clues about its function, its suitability as a drug, vaccine or diagnostic target and aids experimental design. The first version of PSORTdb provided a valuable resource comprising a data set of proteins of known SCL (ePSORTdb) as well as pre-computed SCL predictions for proteomes derived from complete bacterial genomes (cPSORTdb). PSORTdb 2.0 (http://db.psort.org) extends user-friendly functionalities, significantly expands ePSORTdb and now contains pre-computed SCL predictions for all prokaryotes--including Archaea and Bacteria with atypical cell wall/membrane structures. cPSORTdb uses the latest version of the SCL predictor PSORTb (version 3.0), with higher genome prediction coverage and functional improvements over PSORTb 2.0, which has been the most precise bacterial SCL predictor available. PSORTdb 2.0 is the first microbial protein SCL database reported to have an automatic updating mechanism to regularly generate SCL predictions for deduced proteomes of newly sequenced prokaryotic organisms. This updating approach uses a novel sequence analysis we developed that detects whether the microbe being analyzed has an outer membrane. This identification of membrane structure permits appropriate SCL prediction in an auto-updated fashion and allows PSORTdb to serve as a practical resource for genome annotation and prokaryotic research.
微生物蛋白的亚细胞定位(SCL)为其功能、作为药物、疫苗或诊断靶点的适用性提供线索,并有助于实验设计。PSORTdb的第一个版本提供了一个宝贵的资源,包括已知SCL的蛋白质数据集(ePSORTdb)以及对来自完整细菌基因组的蛋白质组的预先计算的SCL预测(cPSORTdb)。PSORTdb 2.0(http://db.psort.org)扩展了用户友好的功能,显著扩展了ePSORTdb,现在包含了对所有原核生物(包括具有非典型细胞壁/膜结构的古细菌和细菌)的预先计算的SCL预测。cPSORTdb使用最新版本的SCL预测器PSORTb(3.0版),与PSORTb 2.0相比,具有更高的基因组预测覆盖率和功能改进,PSORTb 2.0是目前最精确的细菌SCL预测器。PSORTdb 2.0是第一个报道具有自动更新机制的微生物蛋白SCL数据库,该机制可定期为新测序的原核生物的推导蛋白质组生成SCL预测。这种更新方法使用了我们开发的一种新颖的序列分析方法,该方法可检测被分析的微生物是否具有外膜。这种膜结构的识别允许以自动更新的方式进行适当的SCL预测,并使PSORTdb成为基因组注释和原核生物研究的实用资源。