PMID:12876315
| Citation |
Juncker, AS, Willenbrock, H, Von Heijne, G, Brunak, S, Nielsen, H and Krogh, A (2003) Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci. 12:1652-62 |
|---|---|
| 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. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/. |
| Links |
PubMed PMC2323952 Online version:10.1110/ps.0303703 |
| Keywords |
Algorithms; Bacterial Proteins/chemistry; Computational Biology; Cytoplasm/metabolism; Databases, Protein; Genomics; Gram-Negative Bacteria/chemistry; Gram-Negative Bacteria/cytology; Gram-Negative Bacteria/metabolism; Lipoproteins/chemistry; Lipoproteins/metabolism; Neural Networks (Computer); Protein Sorting Signals/physiology; Protein Structure, Secondary; Protein Structure, Tertiary |
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