title: Kernel-based feature selection techniques for transport proteins based on star graph topological indices.
abstract: The transport of the molecules inside cells is a very important topic, especially in Drug Metabolism. The experimental testing of the new proteins for the transporter molecular function is expensive and inefficient due to the large amount of new peptides. Therefore, there is a need for cheap and fast theoretical models to predict the transporter proteins. In the current work, the primary structure of a protein is represented as a molecular Star graph, characterized by a series of topological indices. The dataset was made up of 2,503 protein chains, out of which 413 have transporter molecular function and 2,090 have no transporter function. These indices were used as input to several classification techniques to find the best Quantitative Structure Activity Relationship (QSAR) model that can evaluate the transporter function of a new protein chain. Among several feature selection techniques, the Support Vector Machine Recursive Feature Elimination allows us to obtain a classification model based on 20 attributes with a true positive rate of 83% and a false positive rate of 16.7%.
authors.affiliation: Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain.
authors.forename: Carlos
authors.initials: C
authors.lastname: Fernandez-Lozano
authors.forename: Marcos
authors.initials: M
authors.lastname: Gestal
authors.forename: Nieves
authors.initials: N
authors.lastname: Pedreira-Souto
authors.forename: Lucian
authors.initials: L
authors.lastname: Postelnicu
authors.forename: Julián
authors.initials: J
authors.lastname: Dorado
authors.forename: Cristian Robert
authors.initials: CR
authors.lastname: Munteanu
mesh_terms: D000818:Animals; D002352:Carrier Proteins / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D002352:Carrier Proteins