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dc.contributor.authorBoone, Kyle
dc.contributor.authorCamarda, Kyle
dc.contributor.authorSpencer, Paulette
dc.contributor.authorTamerler, Candan
dc.date.accessioned2019-11-25T14:55:04Z
dc.date.available2019-11-25T14:55:04Z
dc.date.issued2018-12-06
dc.identifier.citationBoone, K., Camarda, K., Spencer, P. et al. Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries. BMC Bioinformatics 19, 469 (2018) doi:10.1186/s12859-018-2514-6en_US
dc.identifier.urihttp://hdl.handle.net/1808/29804
dc.description.abstractBackground Antimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.

Results By generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.

Conclusions We developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.
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dc.description.sponsorshipresearch grants R01DE022054en_US
dc.description.sponsorship3R01DE02205404S1en_US
dc.description.sponsorshipR01DE025476 from the National Institute of Dental and Craniofacial Researchen_US
dc.description.sponsorshipNational Institute of Arthritis and Musculoskeletal and Skin Diseases R21AR062249en_US
dc.publisherBMCen_US
dc.rightsOpen Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectantibacterial peptidesen_US
dc.subjectclassificationen_US
dc.subjectMachine Learningen_US
dc.subjectphysicochemical propertiesen_US
dc.subjectRough set Theoryen_US
dc.subjectsequence similarityen_US
dc.subjectSupervised learningen_US
dc.subjectfunctional peptide searchen_US
dc.titleAntimicrobial peptide similarity and classification through rough set theory using physicochemical boundariesen_US
dc.typeArticleen_US
kusw.kuauthorTamerler, Candan
kusw.kudepartmentMechanical Engineeringen_US
dc.identifier.doi10.1186/s12859-018-2514-6en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1960-2218en_US
kusw.oaversionScholarly/refereed, author accepted manuscripten_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as: Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.