title: Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins.
abstract: Predicting the localization of a protein has become a useful practice for inferring its function. Most of the reported methods to predict subcellular localizations in Gram-negative bacterial proteins make use of standard protein representations that generally do not take into account the distribution of the amino acids and the structural information of the proteins. Here, we propose a protein representation based on the structural information contained in the pairwise statistical contact potentials. The wavelet transform decodes the information contained in the primary structure of the proteins, allowing the identification of patterns along the proteins, which are used to characterize the subcellular localizations. Then, a support vector machine classifier is trained to categorize them. Cellular compartments like periplasm and extracellular medium are difficult to predict, having a high false negative rate. The wavelet-based method achieves an overall high performance while maintaining a low false negative rate, particularly, on "periplasm" and "extracellular medium". Our results suggest the proposed protein characterization is a useful alternative to representing and predicting protein sequences over the classical and cutting edge protein depictions.
authors.affiliation: Signal Processing and Recognition Group, Universidad Nacional de Colombia, s. Manizales, Campus La Nubia, km 7 via al Magdalena, Manizales, Colombia; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, 3501 Fifth Ave, Pittsburgh, PA 15260, USA. Electronic address: gaarangoa@unal.edu.co.
authors.forename: G A
authors.initials: GA
authors.lastname: Arango-Argoty
authors.affiliation: Signal Processing and Recognition Group, Universidad Nacional de Colombia, s. Manizales, Campus La Nubia, km 7 via al Magdalena, Manizales, Colombia; Research Center of the Instituto Tecnologico Metropolitano, Calle 73 No 76A-354, Medellín, Colombia.
authors.forename: J A
authors.initials: JA
authors.lastname: Jaramillo-Garzón
authors.affiliation: Signal Processing and Recognition Group, Universidad Nacional de Colombia, s. Manizales, Campus La Nubia, km 7 via al Magdalena, Manizales, Colombia.
authors.forename: G
authors.initials: G
authors.lastname: Castellanos-Domínguez
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001426:Bacterial Proteins / Q000378:metabolism*; D030562:Databases, Protein; D006090:Gram-Negative Bacteria / Q000378:metabolism*; D017434:Protein Structure, Tertiary; D021381:Protein Transport; D012372:ROC Curve; D013223:Statistics as Topic; D013347:Subcellular Fractions / Q000378:metabolism; D060388:Support Vector Machine; D058067:Wavelet Analysis
chemical_list: D001426:Bacterial Proteins
keywords: Continuous wavelet transform; Feature extraction; Hidden Markov models; Statistical contact potentials; Support vector machines