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      Prediction of heme binding residues from protein sequences with integrative sequence profiles

      research-article
      1 , 1 , , 1 , 1
      Proteome Science
      BioMed Central
      IEEE International Conference on Bioinformatics and Biomedicine 2011
      12-15 November 2011

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          Abstract

          Background

          The heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.

          Methods

          We propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.

          Results

          Our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.

          Conclusions

          The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

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          Most cited references27

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          AAindex: amino acid index database.

          AAindex is a database of amino acid indices and amino acid mutation matrices. An amino acid index is a set of 20 numerical values representing various physico--chemical and biochemical properties of amino acids. An amino acid mutation matrix is generally 20 x 20 numerical values representing similarity of amino acids. AAindex consists of two sections: AAindex1 for the collection of published amino acid indices and AAindex2 for the collection of published amino acid mutation matrices. Each entry of either AAindex1 or AAindex2 consists of the definition, the reference information, a list of related entries in terms of the correlation coefficient and the actual data. The database may be accessed through the DBGET/LinkDB system at GenomeNet (http://www. genome.ad.jp/aaindex/ ) or may be downloaded by anonymous FTP (ftp://ftp.genome.ad.jp/db/genomenet/aaindex/ ).
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            AAindex: amino acid index database, progress report 2008

            AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. We have added a collection of protein contact potentials to the AAindex as a new section. Accordingly AAindex consists of three sections now: AAindex1 for the amino acid index of 20 numerical values, AAindex2 for the amino acid substitution matrix and AAindex3 for the statistical protein contact potentials. All data are derived from published literature. The database can be accessed through the DBGET/LinkDB system at GenomeNet (http://www.genome.jp/dbget-bin/www_bfind?aaindex) or downloaded by anonymous FTP (ftp://ftp.genome.jp/pub/db/community/aaindex/).
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              Predicting the secondary structure of globular proteins using neural network models.

              We present a new method for predicting the secondary structure of globular proteins based on non-linear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a testing set of proteins non-homologous with the corresponding training set was 64.3% on three types of secondary structure (alpha-helix, beta-sheet, and coil), with correlation coefficients of C alpha = 0.41, C beta = 0.31 and Ccoil = 0.41. These quality indices are all higher than those of previous methods. The prediction accuracy for the first 25 residues of the N-terminal sequence was significantly better. We conclude from computational experiments on real and artificial structures that no method based solely on local information in the protein sequence is likely to produce significantly better results for non-homologous proteins. The performance of our method of homologous proteins is much better than for non-homologous proteins, but is not as good as simply assuming that homologous sequences have identical structures.
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                Author and article information

                Conference
                Proteome Sci
                Proteome Sci
                Proteome Science
                BioMed Central
                1477-5956
                2012
                21 June 2012
                : 10
                : Suppl 1
                : S20
                Affiliations
                [1 ]School of Computer, Wuhan University, Wuhan 430072, China
                Article
                1477-5956-10-S1-S20
                10.1186/1477-5956-10-S1-S20
                3380730
                22759579
                f1188769-c4df-4979-bacf-a953179868a6
                Copyright ©2012 Xiong 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.

                IEEE International Conference on Bioinformatics and Biomedicine 2011
                Atlanta, GA, USA
                12-15 November 2011
                History
                Categories
                Proceedings

                Molecular biology
                Molecular biology

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