ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.
Reads0
Chats0
TLDR
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.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/.read more
Citations
More filters
Journal ArticleDOI
Overexpression of a cytosolic glyceraldehyde-3-phosphate dehydrogenase gene OsGAPC3 confers salt tolerance in rice
TL;DR: Results indicate that OsGAPC3 plays important roles in salt stress tolerance in rice and could alleviate the salt toxicity through the regulation of hydrogen peroxide levels.
Journal ArticleDOI
Arabidopsis cytochrome P450s through the looking glass: a window on plant biochemistry
TL;DR: This review surveys historical and evolutionary aspects of P450 studies, expression variations among Arabidopsis P450 loci, catalytic site regions critical for substrate recognition and, finally, genetic mutations/disruptions that can ultimately tie biochemical reactions to physiological functions in a manner not yet possible in most other organisms.
Journal ArticleDOI
Proteomic Characterization of the Small Subunit of Chlamydomonas reinhardtii Chloroplast Ribosome Identification of a Novel S1 Domain–Containing Protein and Unusually Large Orthologs of Bacterial S2, S3, and S5
Kenichi Yamaguchi,Susana Prieto,María Verónica Beligni,Paul A. Haynes,W. Hayes McDonald,John R. Yates,Stephen P. Mayfield +6 more
TL;DR: A proteomic analysis of the plastid ribosomal proteins in the small subunit of the chloroplast ribosome from the green alga Chlamydomonas reinhardtii found that the additional domains of S2, S3, and S5 are located adjacent to each other on the solvent side near the binding site of the S1 protein.
Journal ArticleDOI
A New Approach for Plant Proteomics Characterization of Chloroplast Proteins of Arabidopsis thaliana by Top-down Mass Spectrometry
TL;DR: Top-down Fourier transform mass spectrometry is applied for the first time to a plant proteome, that of the model plant Arabidopsis thaliana, and the previously predicted cleavage site for loss of the signal peptide was found to be incorrect.
Journal ArticleDOI
Analysis of Euglena gracilis Plastid-Targeted Proteins Reveals Different Classes of Transit Sequences
Dion G. Durnford,Michael W. Gray +1 more
TL;DR: This work represents the most comprehensive description to date of transit peptides in Euglena and hints at the complex routes of plastid targeting that must exist in this organism.
References
More filters
Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
TL;DR: A new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence that performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets.
SHORT COMMUNICATION Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites
TL;DR: In this paper, a new method for the identification of in performance compared with the weight matrix method signal peptides and their cleavage sites based on neural (Arrigo et al., 1991; Ladunga et al, 1991; Schneider and networks trained on separate sets of prokaryotic and eukaryotic sequence.
Related Papers (5)
Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana
Steven J. Clough,Andrew F. Bent +1 more