Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary informa

Background: In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins. Results: The models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with av...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Mamoon Rashid, Sudipto Saha, Gajendra P. S. Raghava
• Keywords: BMCBI 2007, Mycobacterial Proteins, Overall Accuracy, Subcellular Location