Prediction of lipoprotein signal peptides in Gram-negative bacteria.
A. S. Juncker1, H. Willenbrock1,
G. von Heijne2, H. Nielsen1, S. Brunak1
and A. Krogh3.
Center for Biological Sequence Analysis, BioCentrum-DTU,
The Technical University of Denmark, DK-2800 Lyngby, Denmark
Department of Biochemistry, Stockholm University,
S-106 91 Stockholm, Sweden
Bioinformatics Centre, University of Copenhagen,
Universitetsparken 15, 2100 Copenhagen, Denmark
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