SPEPlip: the detection of signal peptide and lipoprotein cleavage sites. Summary: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins. Availability: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/

References in zbMATH (referenced in 3 articles )

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  1. Massahi, Aslan; Çalık, Pınar: In-silico determination of \textitPichiapastoris signal peptides for extracellular recombinant protein production (2015)
  2. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  3. Choo, Khar Heng; Tan, Tin Wee; Ranganathan, Shoba: A comprehensive assessment of N-terminal signal peptides prediction methods (2009) ioport