Review of QSAR models for enzyme classes of drug targets: Theoretical background and applications in parasites, hosts, and other organisms

Current Pharmaceutical Design
Riccardo ConcuHumberto González-Díaz

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. 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 web sites. 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/).

Citations

Nov 2, 2011·Journal of Industrial Microbiology & Biotechnology·Wei ChenHao Lin
Aug 3, 2013·Biochimica Et Biophysica Acta·Bing NiuJinyuan Yin
Jun 13, 2015·FEMS Yeast Research·Abraham Abera GebreYoshikazu Ohya
Sep 18, 2012·Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases·Shanmugam Anusuya, Jeyakumar Natarajan

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