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      Prediction of lipoprotein signal peptides in Gram-negative bacteria.

      Protein Science : A Publication of the Protein Society

      Protein Structure, Tertiary, Protein Structure, Secondary, physiology, Protein Sorting Signals, Neural Networks (Computer), metabolism, chemistry, Lipoproteins, cytology, Gram-Negative Bacteria, Genomics, Databases, Protein, Cytoplasm, Computational Biology, Bacterial Proteins, Algorithms

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          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/.

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          Author and article information

          Journal
          10.1110/ps.0303703
          2323952
          12876315

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