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Article abstract


REFERENCE

Prediction of lipoprotein signal peptides in Gram-negative bacteria.
A. S. Juncker1, H. Willenbrock1, G. von Heijne2, H. Nielsen1, S. Brunak1 and A. Krogh3.

1 Center for Biological Sequence Analysis, BioCentrum-DTU, The Technical University of Denmark, DK-2800 Lyngby, Denmark
2 Department of Biochemistry, Stockholm University, S-106 91 Stockholm, Sweden
3 Bioinformatics Centre, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark



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 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.

PMID: 12876315




CORRESPONDENCE

Agnieszka S. Juncker,