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      HECTAR: A method to predict subcellular targeting in heterokonts

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      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          The heterokonts are a particularly interesting group of eukaryotic organisms; they include many key species of planktonic and coastal algae and several important pathogens. To understand the biology of these organisms, it is necessary to be able to predict the subcellular localisation of their proteins but this is not straightforward, particularly in photosynthetic heterokonts which possess a complex chloroplast, acquired as the result of a secondary endosymbiosis. This is because the bipartite target peptides that deliver proteins to these chloroplasts can be easily confused with the signal peptides of secreted proteins, causing currently available algorithms to make erroneous predictions. HECTAR, a subcellular targeting prediction method which takes into account the specific properties of heterokont proteins, has been developed to address this problem.

          Results

          HECTAR is a statistical prediction method designed to assign proteins to five different categories of subcellular targeting: Signal peptides, type II signal anchors, chloroplast transit peptides, mitochondrion transit peptides and proteins which do not possess any N-terminal target peptide. The recognition rate of HECTAR is 96.3%, with Matthews correlation coefficients ranging from 0.67 to 0.95. The method is based on a hierarchical architecture which implements the divide and conquer approach to identify the different possible target peptides one at a time. At each node of the hierarchy, the most relevant outputs of various existing subcellular prediction methods are combined by a Support Vector Machine.

          Conclusion

          The HECTAR method is able to predict the subcellular localisation of heterokont proteins with high accuracy. It also efficiently predicts the subcellular localisation of proteins from cryptophytes, a group that is phylogenetically close to the heterokonts. A variant of HECTAR, called HECTAR SEC , can be used to identify signal peptide and type II signal anchor sequences in proteins from any eukaryotic organism. Both HECTAR and HECTAR SEC are available as a web application at the following address: http://www.sb-roscoff.fr/hectar/.

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          Most cited references38

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            Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

            Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.
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              Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis.

              Draft genome sequences have been determined for the soybean pathogen Phytophthora sojae and the sudden oak death pathogen Phytophthora ramorum. Oömycetes such as these Phytophthora species share the kingdom Stramenopila with photosynthetic algae such as diatoms, and the presence of many Phytophthora genes of probable phototroph origin supports a photosynthetic ancestry for the stramenopiles. Comparison of the two species' genomes reveals a rapid expansion and diversification of many protein families associated with plant infection such as hydrolases, ABC transporters, protein toxins, proteinase inhibitors, and, in particular, a superfamily of 700 proteins with similarity to known oömycete avirulence genes.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                23 September 2008
                : 9
                : 393
                Affiliations
                [1 ]UPMC Univ Paris 6, UMR 7139 Végétaux marins et Biomolécules, Station Biologique, F 29682, Roscoff, France
                [2 ]CNRS, UMR 7139 Végétaux marins et Biomolécules, Station Biologique, F 29682, Roscoff, France
                [3 ]LORIA-CNRS, Campus Scientifique, BP 239, 54506 Vandœuvre-lès-Nancy cedex, France
                Article
                1471-2105-9-393
                10.1186/1471-2105-9-393
                2567999
                18811941
                2d67f0a8-f53c-4c06-ac13-6b1ce8f0bf6b
                Copyright © 2008 Gschloessl et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 March 2008
                : 23 September 2008
                Categories
                Methodology Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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