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

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 ( 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.

Antioxidative and Amylase Inhibitor Peptides from Basil Seeds

“…De novo sequencing of the peptides was then performed using PEAKS studio v.6.0. The potential peptides were subsequently filtered using Peptide Ranker with a score more than 0.8 (Mooney et al 2012) in order to obtain the bioactive components.…”

ACE Inhibitory and Antioxidant Activities of Collagen Hydrolysates from the Ribbon Jellyfish (Chrysaora sp.)

“…Using mass spectroscopy and PEAKS studio so ware v. 6.0, peptide sequences present in the samples were identifi ed. Next, by applying the results to PeptideRanker so ware (screening of peptides with the certainty of more than 50 %) (35), peptide sequences that may have high antioxidant and ACE inhibitory were identifi ed (Table 2).…”

Proteomic analysis of sweet algerian apricot kernels (Prunus armeniaca L.) by combinatorial peptide ligand libraries and LC–MS/MS

“…A score value was assigned to each peptide using N-to-1 neural network probability: peptides showing score values higher than 0.5 were considered to be potentially bioactive. In a probability range from 0 to 1, predicted values closest to 1 indicate a more confident prediction that the candidate resembles a bioactive peptide (Mooney, Haslam, Pollastri, & Shields, 2012). The total number of predicted peptides after in silico digestion was 10 for Prunin 1 and 14 for Prunin 2 ( Table 2).…”

“…A prediction of potential bioactive peptides encrypted in these proteins was obtained by combining different in-silico enzymatic digestions using the software tool PDMQ -Protein Digestion Multi Query (Haraszi, Tasi, Juhasz & Makai, 2015) in order to simulate gastrointestinal processes: pepsin (pH 1.3) was the first enzyme followed by trypsin and chymotrypsin. All generated peptides were then ranked using the tool PeptideRanker (Mooney, Haslam, Pollastri, & Shields, 2012) in order to evaluate the quality of these results. For ranking these peptides based on the probability of being bioactive, N-to-1 neural algorithm was used to produce a list of probability scores (Supplementary Table S2b).…”