NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins

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 nonclassically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequencebased 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....

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Daniel Restrepo-Montoya, Camilo Pino, Luis F. Niño, Manuel E. Patarroyo, Manuel A. Patarroyo
• Keywords: Artificial Intelligence, BMCBI 2011, Proteins, Secretion Mechanism, Support Vector Machines