Abstract
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.
Highlights
Enzymes are biomacromolecules that catalyze almost all the chemical reactions essential for the life of a cell [1]
The high selectivity or specificity of enzymes was likened to the “lock-and-key” model, implying that an accurate fit is required between the active site of an enzyme and its substrate for the catalytic reaction to occur
As summarized in a comprehensive review [33] and demonstrated by a series of recent publications [34,35,36,37], to successfully develop the desired method, we need to consider the following procedures: (i) construct or select a valid benchmark dataset to train and test the predictor; (ii) denote the drug-enzyme samples with an effective formulation that can truly reflect their intrinsic relation with the target to be predicted; (iii) introduce or develop a powerful algorithm to operate the prediction; (iv) conduct a rigorous cross-validation to objectively evaluate its anticipated accuracy; (v) establish a user-friendly web-server for the predictor that is freely accessible to the public
Summary
Enzymes are biomacromolecules that catalyze almost all the chemical reactions essential for the life of a cell [1]. The number of templates for developing high quality 3D structures by structural bioinformatics is very limited It would save us a lot of time and money if we could identify the interactions between enzymes and drugs before carrying out any intense study in this regard. As summarized in a comprehensive review [33] and demonstrated by a series of recent publications [34,35,36,37], to successfully develop the desired method, we need to consider the following procedures: (i) construct or select a valid benchmark dataset to train and test the predictor; (ii) denote the drug-enzyme samples with an effective formulation that can truly reflect their intrinsic relation with the target to be predicted; (iii) introduce or develop a powerful algorithm (or engine) to operate the prediction; (iv) conduct a rigorous cross-validation to objectively evaluate its anticipated accuracy; (v) establish a user-friendly web-server for the predictor that is freely accessible to the public. Let us elaborate how to deal with these procedures one by one
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