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- Emanuelsson, OlofStockholms unviersitet (author)
ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.
- Article/chapterEnglish1999
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- Wiley,1999
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:kth-50450
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-50450URI
- https://doi.org/10.1110/ps.8.5.978DOI
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- Language:English
- Summary in:English
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- 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/.
Subject headings and genre
- NATURVETENSKAP Biologi Bioinformatik och systembiologi hsv//swe
- NATURAL SCIENCES Biological Sciences Bioinformatics and Systems Biology hsv//eng
- chloroplast
- cleavage site
- neural networks
- protein sorting
- transit peptide
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- Nielsen, HStockholms universitet (author)
- von Heijne, GStockholms universitet (author)
- Stockholms unviersitetStockholms universitet (creator_code:org_t)
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- In:Protein Science: Wiley8:5, s. 978-9840961-83681469-896X
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