A theoretical approach to spot active regions in antimicrobial proteins

<p>Abstract</p> <p>Background</p> <p>Much effort goes into identifying new antimicrobial compounds able to evade the increasing resistance of microorganisms to antibiotics. One strategy relies on antimicrobial peptides, either derived from fragments released by proteoly...

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Main Authors: Boix Ester, Nogués Victòria M, Torrent Marc
Format: Article
Language:English
Published: BMC 2009-11-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/10/373
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spelling doaj-56d8e6a228134251b18c5e4387081cd62020-11-25T00:52:16ZengBMCBMC Bioinformatics1471-21052009-11-0110137310.1186/1471-2105-10-373A theoretical approach to spot active regions in antimicrobial proteinsBoix EsterNogués Victòria MTorrent Marc<p>Abstract</p> <p>Background</p> <p>Much effort goes into identifying new antimicrobial compounds able to evade the increasing resistance of microorganisms to antibiotics. One strategy relies on antimicrobial peptides, either derived from fragments released by proteolytic cleavage of proteins or designed from known antimicrobial protein regions.</p> <p>Results</p> <p>To identify these antimicrobial determinants, we developed a theoretical approach that predicts antimicrobial proteins from their amino acid sequence in addition to determining their antimicrobial regions. A bactericidal propensity index has been calculated for each amino acid, using the experimental data reported from a high-throughput screening assay as reference. Scanning profiles were performed for protein sequences and potentially active stretches were identified by the best selected threshold parameters. The method was corroborated against positive and negative datasets. This successful approach means that we can spot active sequences previously reported in the literature from experimental data for most of the antimicrobial proteins examined.</p> <p>Conclusion</p> <p>The method presented can correctly identify antimicrobial proteins with an accuracy of 85% and a sensitivity of 90%. The method can also predict their key active regions, making this a tool for the design of new antimicrobial drugs.</p> http://www.biomedcentral.com/1471-2105/10/373
collection DOAJ
language English
format Article
sources DOAJ
author Boix Ester
Nogués Victòria M
Torrent Marc
spellingShingle Boix Ester
Nogués Victòria M
Torrent Marc
A theoretical approach to spot active regions in antimicrobial proteins
BMC Bioinformatics
author_facet Boix Ester
Nogués Victòria M
Torrent Marc
author_sort Boix Ester
title A theoretical approach to spot active regions in antimicrobial proteins
title_short A theoretical approach to spot active regions in antimicrobial proteins
title_full A theoretical approach to spot active regions in antimicrobial proteins
title_fullStr A theoretical approach to spot active regions in antimicrobial proteins
title_full_unstemmed A theoretical approach to spot active regions in antimicrobial proteins
title_sort theoretical approach to spot active regions in antimicrobial proteins
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2009-11-01
description <p>Abstract</p> <p>Background</p> <p>Much effort goes into identifying new antimicrobial compounds able to evade the increasing resistance of microorganisms to antibiotics. One strategy relies on antimicrobial peptides, either derived from fragments released by proteolytic cleavage of proteins or designed from known antimicrobial protein regions.</p> <p>Results</p> <p>To identify these antimicrobial determinants, we developed a theoretical approach that predicts antimicrobial proteins from their amino acid sequence in addition to determining their antimicrobial regions. A bactericidal propensity index has been calculated for each amino acid, using the experimental data reported from a high-throughput screening assay as reference. Scanning profiles were performed for protein sequences and potentially active stretches were identified by the best selected threshold parameters. The method was corroborated against positive and negative datasets. This successful approach means that we can spot active sequences previously reported in the literature from experimental data for most of the antimicrobial proteins examined.</p> <p>Conclusion</p> <p>The method presented can correctly identify antimicrobial proteins with an accuracy of 85% and a sensitivity of 90%. The method can also predict their key active regions, making this a tool for the design of new antimicrobial drugs.</p>
url http://www.biomedcentral.com/1471-2105/10/373
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