ChloroP, a neural network-based method for predicting chloroplast
transit peptides and their cleavage sites.
Henrik Nielsen1,2, and
Gunnar von Heijne1
Protein Science: 8: 978-984, 1999.
Department of Biochemistry, Stockholm University,
S-106 91 Stockholm, Sweden
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.