alexa ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.
Microbiology

Microbiology

Journal of Antivirals & Antiretrovirals

Author(s): Emanuelsson O, Nielsen H, von Heijne G

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Abstract 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 http://www.cbs.dtu.dk/services/ChloroP/.
This article was published in Protein Sci and referenced in Journal of Antivirals & Antiretrovirals

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