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BMC Bioinformatics

, 10:373

First Online: 11 November 2009Received: 10 March 2009Accepted: 11 November 2009

Abstract

BackgroundMuch 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.

ResultsTo 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.

ConclusionThe 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-10-373 contains supplementary material, which is available to authorized users.

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Author: Marc Torrent - Victòria M Nogués - Ester Boix

Source: https://link.springer.com/







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