CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, a...

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Autores principales:	William F Porto, Állan S Pires, Octavio L Franco
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f25970d176474efdbb8d14d851d85875
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spelling	oai:doaj.org-article:f25970d176474efdbb8d14d851d858752021-11-18T08:05:35ZCS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.1932-620310.1371/journal.pone.0051444https://doaj.org/article/f25970d176474efdbb8d14d851d858752012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23240023/?tool=EBIhttps://doaj.org/toc/1932-6203The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at <http://sourceforge.net/projects/csamppred/> and runs on any Linux machine.William F PortoÁllan S PiresOctavio L FrancoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 12, p e51444 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q William F Porto Állan S Pires Octavio L Franco CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
description	The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at <http://sourceforge.net/projects/csamppred/> and runs on any Linux machine.
format	article
author	William F Porto Állan S Pires Octavio L Franco
author_facet	William F Porto Állan S Pires Octavio L Franco
author_sort	William F Porto
title	CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
title_short	CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
title_full	CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
title_fullStr	CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
title_full_unstemmed	CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
title_sort	cs-amppred: an updated svm model for antimicrobial activity prediction in cysteine-stabilized peptides.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/f25970d176474efdbb8d14d851d85875
work_keys_str_mv	AT williamfporto csamppredanupdatedsvmmodelforantimicrobialactivitypredictionincysteinestabilizedpeptides AT allanspires csamppredanupdatedsvmmodelforantimicrobialactivitypredictionincysteinestabilizedpeptides AT octaviolfranco csamppredanupdatedsvmmodelforantimicrobialactivitypredictionincysteinestabilizedpeptides
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CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.

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