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Bioinformatics Vol. 19 no. 18 2003
pages 2498-2499
© 2003 Oxford University Press

Applications Note

SPEPlip: the detection of signal peptide and lipoprotein cleavage sites

Piero Fariselli , Giacomo Finocchiaro and Rita Casadio *

Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy

Received on April 11, 2003 ; revised on June 4, 2003 ; accepted on July 6, 2003

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/

Contact: piero{at}biocomp.unibo.it

* To whom correspondence should be addressed.


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