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Prediction of lipoprotein signal peptides in Gram-positive bacteria with a Hidden Markov Model.

We present a Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides. PRED-LIPO is freely available at http://bioinformatics.biol.uoa.gr/PRED-LIPO/, and we anticipate that it will be a valuable tool for the experimentalists studying secreted proteins and lipoproteins from Gram-positive bacteria.

Pubmed ID: 19367716


  • Bagos PG
  • Tsirigos KD
  • Liakopoulos TD
  • Hamodrakas SJ


Journal of proteome research

Publication Data

December 15, 2008

Associated Grants


Mesh Terms

  • Algorithms
  • Amino Acid Sequence
  • Bacterial Proteins
  • Computational Biology
  • Cytoplasm
  • Databases, Protein
  • Gram-Positive Bacteria
  • Lipoproteins
  • Markov Chains
  • Molecular Sequence Data
  • Protein Sorting Signals
  • Protein Structure, Tertiary
  • Reproducibility of Results
  • Software