SPEPlip: the detection of signal peptide and lipoprotein cleavage sites.
CONCLUSIONS
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.


BACKGROUND
It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/
Fariselli
Piero
P
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. piero@biocomp.unibo.it
Finocchiaro
Giacomo
G
Casadio
Rita
R
D000465:Algorithms; D001185:Artificial Intelligence; D008074:Lipoproteins / Q000737:chemistry*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D021382:Protein Sorting Signals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D008074:Lipoproteins; D021382:Protein Sorting Signals