Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

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dc.contributor.author Mooney, Catherine
dc.contributor.author Haslam, Niall J.
dc.contributor.author Pollastri, Gianluca
dc.contributor.author Shields, Denis C.
dc.date.accessioned 2012-11-07T15:27:40Z
dc.date.available 2012-11-07T15:27:40Z
dc.date.copyright 2012 Mooney et al en
dc.date.issued 2012-10
dc.identifier.citation PLoS ONE en
dc.identifier.uri http://hdl.handle.net/10197/3891
dc.description.abstract The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides.We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4-20 amino acids) and one focused on long peptides (>20 amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure.We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive. en
dc.description.sponsorship Science Foundation Ireland en
dc.language.iso en en
dc.publisher PLOS en
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. en
dc.subject Peptides en
dc.subject Bioactivity en
dc.subject.lcsh Bioactive compounds en
dc.subject.lcsh Peptides en
dc.title Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity en
dc.type Journal Article en
dc.internal.authorcontactother niall.haslam@ucd.ie
dc.internal.availability Full text available en
dc.status Peer reviewed en
dc.identifier.volume 7 en
dc.identifier.issue 10 en
dc.identifier.startpage e45012 en
dc.identifier.doi 10.1371/journal.pone.0045012
dc.neeo.contributor Mooney|Catherine|aut|
dc.neeo.contributor Haslam|Niall J.|aut|
dc.neeo.contributor Pollastri|Gianluca|aut|
dc.neeo.contributor Shields|Denis C.|aut|
dc.description.othersponsorship SFI 08/IN.1/B1864 10/RFP/GEN2749 en
dc.description.admin Author has checked copyright en
dc.description.admin DG 11/10/12 en
dc.internal.rmsid 310196408
dc.date.updated 2012-10-11T10:26:17Z


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