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 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 three-dimensional 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.

References in zbMATH (referenced in 15 articles )

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  1. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  2. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  3. Nasiri, Jaber; Naghavi, Mohammad Reza; Kayvanjoo, Amir Hossein; Nasiri, Mojtaba; Ebrahimi, Mansour: Precision assessment of some supervised and unsupervised algorithms for genotype discrimination in the genus \textitpisumusing SSR molecular data (2015)
  4. Ruiz-Blanco, Yasser B.; Marrero-Ponce, Yovani; Prieto, Pablo J.; Salgado, Jesús; García, Yamila; Sotomayor-Torres, Clivia M.: A Hooke’s law-based approach to protein folding rate (2015)
  5. Xu, Yan; Ding, Ya-Xin; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang: Phogly-PseAAC: prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity (2015)
  6. Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (2014)
  7. Chen, Xiao; Peng, Qinke; Han, Libin; Zhong, Tao; Xu, Tao: An effective haplotype assembly algorithm based on hypergraph partitioning (2014)
  8. Lin, Thy-Hou; Tsai, Tsung-Lin: Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM (2014)
  9. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  10. Mondal, Sukanta; Pai, Priyadarshini P.: Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction (2014)
  11. Nanni, Loris; Lumini, Alessandra; Brahnam, Sheryl: A set of descriptors for identifying the protein-drug interaction in cellular networking (2014)
  12. Pavesi, Angelo: Prediction of the determinants of thermal stability by linear discriminant analysis: the case of the glutamate dehydrogenase protein family (2014)
  13. Podder, Avijit; Jatana, Nidhi; Latha, N.: Human dopamine receptors interaction network (DRIN): a systems biology perspective on topology, stability and functionality of the network (2014)
  14. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization (2014)
  15. Yang, Lei; Lv, Yingli; Li, Tao; Zuo, Yongchun; Jiang, Wei: Human proteins characterization with subcellular localizations (2014)