Abstract
Abstract 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/
Keywords
Signal peptideFalse positive paradoxPeptideCleavage (geology)Pattern recognition (psychology)Set (abstract data type)SIGNAL (programming language)Computational biologySortingComputer scienceArtificial intelligenceBiologyBiochemistryPeptide sequenceAlgorithmGene
MeSH Terms
AlgorithmsArtificial IntelligenceLipoproteinsNeural NetworksComputerPattern RecognitionAutomatedProtein Sorting SignalsReproducibility of ResultsSensitivity and SpecificitySequence AlignmentSequence AnalysisProteinSoftware
Affiliated Institutions
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Publication Info
- Year
- 2003
- Type
- article
- Volume
- 19
- Issue
- 18
- Pages
- 2498-2499
- Citations
- 68
- Access
- Closed
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Cite This
Piero Fariselli,
Giacomo Finocchiaro,
Rita Casadio
(2003).
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites.
Bioinformatics
, 19
(18)
, 2498-2499.
https://doi.org/10.1093/bioinformatics/btg360
Identifiers
- DOI
- 10.1093/bioinformatics/btg360
- PMID
- 14668245