Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci (2003)
| Citations: | 97 - 0 self |
BibTeX
@MISC{Juncker03predictionof,
author = {Agnieszka S. Juncker and Hanni Willenbrock and Gunnar Von Heijne and Søren Brunak and Henrik Nielsen and Anders Krogh},
title = {Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci},
year = {2003}
}
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Abstract
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 Gramnegatives, 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/.
Keyphrases
lipoprotein signal peptide gram-negative bacteria protein sci transmembrane protein similar result gram-positive lipoprotein signal peptide spaseii-cleaved protein gram-positive genome spasei-cleaved protein gram-negative genome neural network-based predictor escherichia coli k12 genome search web server dk service lipop hidden markov model cytoplasmic protein verified lipoprotein false positive gram-positive test set new experimental data gram-negative eubacteria
