Review of QSAR Models for Enzyme Classes of Drug Targets: Theoretical Background and Applications in Parasites, Hosts and Other Organisms
Abstract: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. nonlinear 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/Bio- AIMS/EnzClassPred.php) and the server EzyPred (http://www.csbio.sjtu.edu.cn/bioinf/EzyPred/).
Keywords: 1D techniques; Alignment-Free Methods (AFMs); Artificial Neural Network; Bayesian classification; EC number; Enzyme Class Query (ECQ); Enzymes classes; Functional Domain Composition (FDC); Gene Ontology; MASCOT; Markov Chain Model; Markov Models; Mouse Genome Database (MGD); Predict Enzyme Function; Protein Data Bank; Protein Structure-Function Relationship; Saccharomyces Genome Database(SGD); Support Vector Machine; Web Servers; alignment-free Machine Learning methods; computational approaches
Document Type: Research Article
Affiliations: Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela (USC), Santiago de Compostela, 15782, Spain.
Publication date: August 1, 2010
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