.: Marcos Gestal :.     -----


Datos de la Publicación:

Carlos Fernandez-Lozano, Enrique Fernández-Blanco, Kirtan Dave, Nieves Pedreira, Marcos Gestal, Julián Dorado, Cristian Robert Munteanu
Título: Improving Enzyme Regulatory Protein Classification by means of SVM-RFE Feature Selection
Revista: Molecular BioSystems
ISSN: 1742-206X
Volumen: 10
Páginas: 1063-1071
Editorial: RSC Publishing
Fecha Publicación: Enero 2014
Factor de Impacto: 3.210
SCIMago Journal Rank: 1.390
PubMed ID: 24556806
doi: 10.1039/C3MB70489K
Categorías WoS: Biochemistry & Molecular Biology - Cuartil: 2 - Tercil: 2 - Posición 110 de 289
Citas ISI: 1
Citas Scopus: 1
Citas Google Scholar: 3


Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties associated with the regulation of enzymatic activities, and their structural diversity creates the necessity for new theoretical methods that can predict the enzyme regulatory function of new proteins. The current work presents the first classification model that predicts protein enzyme regulators using the Markov mean properties. These protein descriptors encode the topological information of the amino acid into contact networks based on amino acid distances and physicochemical properties. MInD-Prot software calculated these molecular descriptors for 2415 protein chains (350 enzyme regulators) using five atom physicochemical properties (Mulliken electronegativity, Kang–Jhon polarizability, vdW area, atom contribution to P) and the protein 3D regions. The best classification models to predict enzyme regulators have been obtained with machine learning algorithms from Weka using 18 features. K* has been demonstrated to be the most accurate algorithm for this protein function classification. Wrapper Subset Evaluator and SVM-RFE approaches were used to perform a feature subset selection with the best results obtained from SVM-RFE. Classification performance employing all the available features can be reached using only the 8 most relevant features selected by SVM-RFE. Thus, the current work has demonstrated the possibility of predicting new molecular targets involved in enzyme regulation using fast theoretical algorithms.