Journal
CURRENT PHARMACEUTICAL DESIGN
Volume 16, Issue 24, Pages 2710-2723Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/138161210792389207
Keywords
Enzymes classes; Protein Structure-Function Relationship; Predict Enzyme Function; Support Vector Machine; Gene Ontology; Markov Models; Web Servers
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Funding
- Faculty of Pharmacy, USC, Spain
- Isidro Parga Pondal programme (Xunta de Galicia)
- European Union
- Regione Autonoma Sardegna-Progetto [CRP2_133]
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The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. Many of these proteins are relevant for Pharmaceutical Design because they may be enzymes of different classes that could become drug targets. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a review and discussion of Alignment-Free Methods (AFMs) for fast prediction of the Enzyme Classification (EC) number from structural patterns. We referred to both methods based on linear techniques such as Linear Discriminant Analysis (LDA) and/or non-linear models like Artificial Neural Networks (ANN) or Support Vector Machine (SVM) in order to compare linear vs. non-linear classifiers. We also detected which of these models have been implemented as Web Servers free to the public and compiled a list of some of these websites. For instance, we reviewed the servers implemented at portal Bio-AIMS (http://miaja.tic.udc.es/BioAIMS/EnzClassPred.php) and the server EzyPred (http://www.csbio.sjtu.edu.cn/bioinf/EzyPred/).
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