title: NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins.
abstract: BACKGROUND
  Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.
  
  
  RESULTS
  Several feature-based classifiers were trained using different sequence transformation vectors (frequencies, dipeptides, physicochemical factors and PSSM) and Support Vector Machines (SVMs) with Linear, Polynomial and Gaussian kernel functions. Nested k-fold cross-validation (CV) was applied to select the best models, using the inner CV loop to tune the model parameters and the outer CV group to compute the error. The parameters and Kernel functions and the combinations between all possible feature vectors were optimized using grid search.
  
  
  CONCLUSIONS
  The final model was tested against an independent set not previously seen by the model, obtaining better predictive performance compared to SecretomeP V2.0 and SecretPV2.0 for the identification of non-classically secreted proteins. NClassG+ is freely available on the web at http://www.biolisi.unal.edu.co/web-servers/nclassgpositive/.
authors.affiliation: School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No, 63C-69, Bogotá DC, Colombia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Restrepo-Montoya
authors.forename: Camilo
authors.initials: C
authors.lastname: Pino
authors.forename: Luis F
authors.initials: LF
authors.lastname: Nino
authors.forename: Manuel E
authors.initials: ME
authors.lastname: Patarroyo
authors.forename: Manuel A
authors.initials: MA
authors.lastname: Patarroyo
mesh_terms: D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000145:classification* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006094:Gram-Positive Bacteria / Q000378:metabolism*; D008962:Models, Theoretical; D012984:Software
chemical_list: D001426:Bacterial Proteins