Type

Journal Article

Authors

Denis C. Shields
Gianluca Pollastri
Niall J. Haslam
Catherine Mooney

Subjects

Chemistry

Topics
drug design bioactive compounds amino acids bioactivity structural features peptide hormones computational biology functional design chemistry amino acid sequence animals peptides bioactive peptides anti infective agents toxins biological reproducibility of results antimicrobial peptides algorithms functional prediction humans databases factual

Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity (2012)

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.
Collections Ireland -> University College Dublin -> CASL Research Collection
Ireland -> University College Dublin -> Institutes and Centres
Ireland -> University College Dublin -> College of Health and Agricultural Sciences
Ireland -> University College Dublin -> Conway Institute
Ireland -> University College Dublin -> Computer Science Research Collection
Ireland -> University College Dublin -> School of Medicine
Ireland -> University College Dublin -> Conway Institute Research Collection
Ireland -> University College Dublin -> Complex and Adaptive Systems Laboratory
Ireland -> University College Dublin -> College of Science
Ireland -> University College Dublin -> School of Computer Science
Ireland -> University College Dublin -> Medicine Research Collection

Full list of authors on original publication

Denis C. Shields, Gianluca Pollastri, Niall J. Haslam, Catherine Mooney

Experts in our system

1
Denis C. Shields
University College Dublin
Total Publications: 123
 
2
Catherine Mooney
University College Dublin
Total Publications: 63