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VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens

BACKGROUND: Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in path...

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Detalles Bibliográficos
Autores principales: Garg, Aarti, Gupta, Dinesh
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2254373/
https://www.ncbi.nlm.nih.gov/pubmed/18226234
http://dx.doi.org/10.1186/1471-2105-9-62
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author Garg, Aarti
Gupta, Dinesh
author_facet Garg, Aarti
Gupta, Dinesh
author_sort Garg, Aarti
collection PubMed
description BACKGROUND: Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens. RESULTS: In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of i(th )and i+1(th )amino acid residues), higher order dipeptide composition (pairs of i(th )and i+2(nd )residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features. CONCLUSION: VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server – VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.
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spelling pubmed-22543732008-02-26 VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens Garg, Aarti Gupta, Dinesh BMC Bioinformatics Methodology Article BACKGROUND: Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens. RESULTS: In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of i(th )and i+1(th )amino acid residues), higher order dipeptide composition (pairs of i(th )and i+2(nd )residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features. CONCLUSION: VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server – VirulentPred, at http://bioinfo.icgeb.res.in/virulent/. BioMed Central 2008-01-28 /pmc/articles/PMC2254373/ /pubmed/18226234 http://dx.doi.org/10.1186/1471-2105-9-62 Text en Copyright ©2008 Garg and Gupta; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Garg, Aarti
Gupta, Dinesh
VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens
title VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens
title_full VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens
title_fullStr VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens
title_full_unstemmed VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens
title_short VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens
title_sort virulentpred: a svm based prediction method for virulent proteins in bacterial pathogens
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2254373/
https://www.ncbi.nlm.nih.gov/pubmed/18226234
http://dx.doi.org/10.1186/1471-2105-9-62
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