Distinct Profiling of Antimicrobial Peptide Families

Handle URI:
http://hdl.handle.net/10754/334971
Title:
Distinct Profiling of Antimicrobial Peptide Families
Authors:
Khamis, Abdullah M. ( 0000-0002-5945-0159 ) ; Essack, Magbubah ( 0000-0003-2709-5356 ) ; Gao, Xin ( 0000-0002-7108-3574 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
Motivation: The increased prevalence of multi-drug resistant (MDR) pathogens heightens the need to design new antimicrobial agents. Antimicrobial peptides (AMPs) exhibit broad-spectrum potent activity against MDR pathogens and kills rapidly, thus giving rise to AMPs being recognized as a potential substitute for conventional antibiotics. Designing new AMPs using current in-silico approaches is, however, challenging due to the absence of suitable models, large number of design parameters, testing cycles, production time and cost. To date, AMPs have merely been categorized into families according to their primary sequences, structures and functions. The ability to computationally determine the properties that discriminate AMP families from each other could help in exploring the key characteristics of these families and facilitate the in-silico design of synthetic AMPs. Results: Here we studied 14 AMP families and sub-families. We selected a specific description of AMP amino acid sequence and identified compositional and physicochemical properties of amino acids that accurately distinguish each AMP family from all other AMPs with an average sensitivity, specificity and precision of 92.88%, 99.86% and 95.96%, respectively. Many of our identified discriminative properties have been shown to be compositional or functional characteristics of the corresponding AMP family in literature. We suggest that these properties could serve as guides for in-silico methods in design of novel synthetic AMPs. The methodology we developed is generic and has a potential to be applied for characterization of any protein family.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Khamis, A. M., Essack, M., Gao, X., & Bajic, V. B. (2014). Distinct Profiling of Antimicrobial Peptide Families. Bioinformatics. doi: 10.1093/bioinformatics/btu738
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
10-Nov-2014
DOI:
10.1093/bioinformatics/btu738
PubMed ID:
25388148
PubMed Central ID:
PMC4380027
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
This work was supported by KAUST Base Research Fund of VBB and KAUST Collaborative Research Funds of XG.
Additional Links:
http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btu738
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKhamis, Abdullah M.en
dc.contributor.authorEssack, Magbubahen
dc.contributor.authorGao, Xinen
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2014-11-16T07:29:25Z-
dc.date.available2014-11-16T07:29:25Z-
dc.date.issued2014-11-10en
dc.identifier.citationKhamis, A. M., Essack, M., Gao, X., & Bajic, V. B. (2014). Distinct Profiling of Antimicrobial Peptide Families. Bioinformatics. doi: 10.1093/bioinformatics/btu738en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.pmid25388148en
dc.identifier.doi10.1093/bioinformatics/btu738en
dc.identifier.urihttp://hdl.handle.net/10754/334971en
dc.description.abstractMotivation: The increased prevalence of multi-drug resistant (MDR) pathogens heightens the need to design new antimicrobial agents. Antimicrobial peptides (AMPs) exhibit broad-spectrum potent activity against MDR pathogens and kills rapidly, thus giving rise to AMPs being recognized as a potential substitute for conventional antibiotics. Designing new AMPs using current in-silico approaches is, however, challenging due to the absence of suitable models, large number of design parameters, testing cycles, production time and cost. To date, AMPs have merely been categorized into families according to their primary sequences, structures and functions. The ability to computationally determine the properties that discriminate AMP families from each other could help in exploring the key characteristics of these families and facilitate the in-silico design of synthetic AMPs. Results: Here we studied 14 AMP families and sub-families. We selected a specific description of AMP amino acid sequence and identified compositional and physicochemical properties of amino acids that accurately distinguish each AMP family from all other AMPs with an average sensitivity, specificity and precision of 92.88%, 99.86% and 95.96%, respectively. Many of our identified discriminative properties have been shown to be compositional or functional characteristics of the corresponding AMP family in literature. We suggest that these properties could serve as guides for in-silico methods in design of novel synthetic AMPs. The methodology we developed is generic and has a potential to be applied for characterization of any protein family.en
dc.description.sponsorshipThis work was supported by KAUST Base Research Fund of VBB and KAUST Collaborative Research Funds of XG.en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btu738en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleDistinct Profiling of Antimicrobial Peptide Familiesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC4380027en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorEssack, Magbubahen
kaust.authorGao, Xinen
kaust.authorBajic, Vladimir B.en
kaust.authorKhamis, Abdullah M.en

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