Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China.
J Theor Biol. 2010 May 21;264(2):326-33. doi: 10.1016/j.jtbi.2010.01.018. Epub 2010 Jan 20.
By incorporating the information of gene ontology, functional domain, and sequential evolution, a new predictor called Gneg-mPLoc was developed. It can be used to identify Gram-negative bacterial proteins among the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. It can also be used to deal with the case when a query protein may simultaneously exist in more than one location. Compared with the original predictor called Gneg-PLoc, the new predictor is much more powerful and flexible. For a newly constructed stringent benchmark dataset in which none of proteins included has >or=25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gneg-mPLoc was 85.5%, which was more than 14% higher than the corresponding rate by the Gneg-PLoc. As a user friendly web-server, Gneg-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/Gneg-multi/.
通过整合基因本体、功能域和序列进化的信息，开发了一种新的预测器，称为 Gneg-mPLoc。它可以用于识别以下八个位置中的革兰氏阴性细菌蛋白：（1）细胞质，（2）细胞外，（3）菌毛，（4）鞭毛，（5）内膜，（6）拟核，（7）外膜和（8）周质。它还可以用于处理查询蛋白可能同时存在于多个位置的情况。与原始的预测器 Gneg-PLoc 相比，新的预测器更加强大和灵活。对于一个新构建的严格基准数据集，其中没有任何蛋白质与同一子集（位置）中的任何其他蛋白质具有>或= 25%的成对序列同一性，Gneg-mPLoc 的总体折刀成功率达到 85.5%，比 Gneg-PLoc 的相应成功率高 14%以上。作为一个用户友好的网络服务器，Gneg-mPLoc 可在 http://www.csbio.sjtu.edu.cn/bioinf/Gneg-multi/ 上免费访问。
