Cargando…

VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

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,...

Descripción completa

Detalles Bibliográficos
Autores principales: Saha, Sudipto, Raghava, G.P.S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054027/
https://www.ncbi.nlm.nih.gov/pubmed/16689701
http://dx.doi.org/10.1016/S1672-0229(06)60015-6
_version_ 1782458510063173632
author Saha, Sudipto
Raghava, G.P.S.
author_facet Saha, Sudipto
Raghava, G.P.S.
author_sort Saha, Sudipto
collection PubMed
description 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/).
format Online
Article
Text
id pubmed-5054027
institution National Center for Biotechnology Information
language English
publishDate 2006
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-50540272016-10-14 VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition Saha, Sudipto Raghava, G.P.S. Genomics Proteomics Bioinformatics Method 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/). Elsevier 2006 2006-04-18 /pmc/articles/PMC5054027/ /pubmed/16689701 http://dx.doi.org/10.1016/S1672-0229(06)60015-6 Text en © 2006 Beijing Institute of Genomics http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
spellingShingle Method
Saha, Sudipto
Raghava, G.P.S.
VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
title VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
title_full VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
title_fullStr VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
title_full_unstemmed VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
title_short VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
title_sort vicmpred: an svm-based method for the prediction of functional proteins of gram-negative bacteria using amino acid patterns and composition
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054027/
https://www.ncbi.nlm.nih.gov/pubmed/16689701
http://dx.doi.org/10.1016/S1672-0229(06)60015-6
work_keys_str_mv AT sahasudipto vicmpredansvmbasedmethodforthepredictionoffunctionalproteinsofgramnegativebacteriausingaminoacidpatternsandcomposition
AT raghavagps vicmpredansvmbasedmethodforthepredictionoffunctionalproteinsofgramnegativebacteriausingaminoacidpatternsandcomposition