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

Published online by Cambridge University Press:  01 May 1999

OLOF EMANUELSSON ,
HENRIK NIELSEN  and
GUNNAR VON HEIJNE
OLOF EMANUELSSON
Affiliation:
Department of Biochemistry, Stockholm University, S-106 91 Stockholm, Sweden
HENRIK NIELSEN
Affiliation:
Department of Biochemistry, Stockholm University, S-106 91 Stockholm, Sweden Center for Biological Sequence Analysis, The Technical University of Denmark, DK-2800 Lyngby, Denmark
GUNNAR VON HEIJNE
Affiliation:
Department of Biochemistry, Stockholm University, S-106 91 Stockholm, Sweden
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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/.

Keywords

chloroplastcleavage siteneural networksprotein sortingtransit peptide
Type
Research Article
Information
Protein Science , Volume 8 , Issue 5 , May 1999 , pp. 978 - 984
Copyright
1999 The Protein Society

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