Article abstract


ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.
Olof Emanuelsson1, Henrik Nielsen1,2, and Gunnar von Heijne1
Protein Science: 8: 978-984, 1999.

1 Department of Biochemistry, Stockholm University, S-106 91 Stockholm, Sweden
2 Center for Biological Sequence Analysis, BioCenterum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark


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 so far only 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 A. 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.


Olof Emanuelsson,