Saha, Sudipto and Raghava, G.P.S. (2006) VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, proteomics & bioinformatics / Beijing Genomics Institute, 4 (1). pp. 42-7. ISSN 1672-0229
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Abstract
In this study, an attempt has been made to predict the major functions of gram-negative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins (255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors). First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins (http://www.imtech.res.in/raghava/vicmpred/).
Item Type: | Article |
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Additional Information: | Copyright of this article belongs to Elsevier Science. |
Uncontrolled Keywords: | virulence factor; cellular process; information molecule; tetrapeptide; VICMpred; gram-negative bacteria |
Subjects: | Q Science > QR Microbiology |
Depositing User: | Dr. K.P.S.Sengar |
Date Deposited: | 09 Jan 2012 04:23 |
Last Modified: | 09 Jan 2012 04:23 |
URI: | http://crdd.osdd.net/open/id/eprint/151 |
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