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ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.
Stockholms unviersitet.ORCID iD: 0000-0002-8879-9245
Stockholms universitet.
Stockholms universitet.
1999 (English)In: Protein Science, ISSN 0961-8368, Vol. 8, no 5, 978-984 p.Article in journal (Refereed) Published
Abstract [en]

We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within +/-2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web-server at

Place, publisher, year, edition, pages
1999. Vol. 8, no 5, 978-984 p.
Keyword [en]
chloroplast, cleavage site, neural networks, protein sorting, transit peptide
National Category
Bioinformatics and Systems Biology
URN: urn:nbn:se:kth:diva-50450DOI: 10.1110/ps.8.5.978ISI: 000080109000004PubMedID: 10338008OAI: diva2:461889
Available from: 2011-12-05 Created: 2011-12-05 Last updated: 2011-12-06Bibliographically approved

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Emanuelsson, Olof
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