Prediction of Lipoprotein Signal Peptides in Gram-negative Bacteria

Protein Sci. 2003 Aug;12(8):1652-62. doi: 10.1110/ps.0303703.

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/.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • 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

Substances

  • Bacterial Proteins
  • Lipoproteins
  • Protein Sorting Signals