iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking.

With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes...

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Publicado en:BioMed Research International Vol. 2013; pp. 701317 - 701318
Autores principales: Min, Jian-Liang, Xiao, Xuan, Chou, Kuo-Chen
Formato: Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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        atl: iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking.
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          Min, Jian-Liang
          Xiao, Xuan
          Chou, Kuo-Chen
        affil: Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China.
      sug:
        subj:
          Amino Acids
          Internet
          Algorithms
          Bioinformatics
          Resource Databases
      ab: With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone. Although some computational methods were developed in this regard based on the knowledge of the three-dimensional structure of enzyme, unfortunately their usage is quite limited because threedimensional structures of many enzymes are still unknown. Here, we reported a sequence-based predictor, called "iEzy-Drug," in which each drug compound was formulated by a molecular fingerprint with 258 feature components, each enzyme by the Chou's pseudo amino acid composition generated via incorporating sequential evolution information and physicochemical features derived from its sequence, and the prediction engine was operated by the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iEzy-Drug via rigorous cross-validations was about 91%. Moreover, to maximize the convenience for the majority of experimental scientists, a user-friendly web server was established, by which users can easily obtain their desired results.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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