Prediction of lipoprotein signal peptides in Gram-negative bacteria.
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/.
Juncker
Agnieszka S
AS
Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby 2800, Denmark.
Willenbrock
Hanni
H
Von Heijne
Gunnar
G
Brunak
Søren
S
Nielsen
Henrik
H
Krogh
Anders
A
D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry*; D019295:Computational Biology; D003593:Cytoplasm / Q000378:metabolism; D030562:Databases, Protein; D023281:Genomics; D006090:Gram-Negative Bacteria / Q000737:chemistry* / Q000166:cytology / Q000378:metabolism*; D008074:Lipoproteins / Q000737:chemistry* / Q000378:metabolism; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals / Q000502:physiology*; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary
D001426:Bacterial Proteins; D008074:Lipoproteins; D021382:Protein Sorting Signals