Prediction of lipoprotein signal peptides in Gram-negative bacteria
Prediction of lipoprotein signal peptides in Gram-negative bacteria
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DOI：
10.1110/ps.0303703
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发表时间：
2003-08-01
期刊：
影响因子：
8
通讯作者：
Krogh, A
中科院分区：
文献类型：
--
作者：
Juncker, AS;Willenbrock, H;Krogh, A
建立了一种预测革兰氏阴性真细菌中脂蛋白信号肽的方法LipoP。隐马尔可夫模型（HMM）能够区分脂蛋白（SPaseII裂解蛋白），SPaseI裂解蛋白，胞质蛋白和跨膜蛋白。该预测器能够正确预测96.8%的脂蛋白，在一组SPaseI切割的、胞质的和跨膜的蛋白中只有0.3%的假阳性。所获得的结果显着优于以前开发的方法。尽管革兰氏阳性脂蛋白信号肽与革兰氏阴性脂蛋白不同，但HMM能够识别革兰氏阳性试验集中92.9%的脂蛋白。对12个革兰氏阴性基因组和1个革兰氏阳性基因组进行基因组搜索。大肠杆菌K12的结果与新的实验数据进行了比较，HMM的预测与实验验证的脂蛋白吻合良好。一个基于神经网络的预测器被开发用于比较，它给出了非常相似的结果。LipoP作为Web服务器可在www.cbs.dtu.dk/services/LipoP/上获得。
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/.