Juncker Agnieszka S, Willenbrock Hanni, Von Heijne Gunnar, Brunak Søren, Nielsen Henrik, Krogh Anders
Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby 2800, Denmark.
Protein Sci. 2003 Aug;12(8):1652-62. doi: 10.1110/ps.0303703.
A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.
一种用于预测革兰氏阴性真细菌中脂蛋白信号肽的方法——LipoP已被开发出来。隐马尔可夫模型（HMM）能够区分脂蛋白（信号肽酶II切割的蛋白质）、信号肽酶I切割的蛋白质、细胞质蛋白和跨膜蛋白。在一组信号肽酶I切割的、细胞质的和跨膜的蛋白质中，该预测器能够正确预测96.8%的脂蛋白，假阳性率仅为0.3%。所得结果明显优于先前开发的方法。尽管革兰氏阳性菌的脂蛋白信号肽与革兰氏阴性菌不同，但HMM能够识别革兰氏阳性测试集中92.9%的脂蛋白。对12个革兰氏阴性菌基因组和1个革兰氏阳性菌基因组进行了全基因组搜索。将大肠杆菌K12的结果与新的实验数据进行了比较，HMM的预测结果与经实验验证的脂蛋白吻合良好。还开发了一种基于神经网络的预测器用于比较，其结果非常相似。LipoP可作为网络服务器在www.cbs.dtu.dk/services/LipoP/上获取。
