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Prediction of lipoprotein signal peptides in Gram‐negative bacteria
Author(s) -
Juncker Agnieszka S.,
Willenbrock Hanni,
von Heijne Gunnar,
Brunak Søren,
Nielsen Henrik,
Krogh Anders
Publication year - 2003
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.0303703
Subject(s) - signal peptide , hidden markov model , transmembrane protein , gram , periplasmic space , computational biology , lipoprotein , biology , false positive paradox , gram negative bacteria , bacterial genome size , genome , peptide sequence , escherichia coli , bacteria , biochemistry , genetics , gene , cholesterol , artificial intelligence , computer science , receptor
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/ .