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Towards the Improved Discovery and Design of
Functional Peptides: Common Features of Diverse
Classes Permit Generalized Prediction of Bioactivity
Catherine Mooney
1,2,3
, Niall J. Haslam
1,2,3
, Gianluca Pollastri
1,4
, Denis C. Shields
1,2,3
*
1Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin, Ireland, 2Conway Institute of Biomolecular and Biomedical Science, University
College Dublin, Belfield, Dublin, Ireland, 3School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Ireland, 4School of Computer Science and
Informatics, University College Dublin, Belfield, Dublin, Ireland
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 (w20 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.
Citation: Mooney C, Haslam NJ, Pollastri G, Shields DC (2012) Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse
Classes Permit Generalized Prediction of Bioactivity. PLoS ONE 7(10): e45012. doi:10.1371/journal.pone.0045012
Editor: Lukasz Kurgan, University of Alberta, Canada
Received March 6, 2012; Accepted August 15, 2012; Published October 8, 2012
Copyright: ß2012 Mooney et al. 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.
Funding: The work was funded through a Science Foundation Ireland principal investigator grant (grant 08/IN.1/B1864) to DCS and a Science Foundation Ireland
research frontiers grant (grant 10/RFP/GEN2749) to GP. (http://www.sfi.ie/). The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: NJH is a PLOS ONE editorial Board member. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and
materials.
* E-mail: denis.shields@ucd.ie
Introduction
Biologically active, or bioactive, peptides encompass a wide
range of activities across all kingdoms of life, and the available
proteomes of many organisms now represent a rich resource for
the computational prediction of potential function of peptides
encoded within them. For example, new antibiotic drugs are
needed urgently to address the problem of bacterial resistance [1]
and bioactive peptides may provide an answer [2,3]. They may
serve as leads for drug design, or in certain circumstances be
themselves used as therapeutics. However, bioactive peptides are
not only important as a potential source of new antibiotic drugs
but have also been shown to have a potential role in the
development of new antiviral, antifungal and antiparasitic drugs
that may be less susceptible to the development of resistance in
pathogens [2]. Bioactive peptides may also modulate human
platelet function [4], be used in the development of biomaterials
[5] and in wound healing [6]. The identification of food, especially
milk, derived bioactive peptides is a growing research area. For
example, milk protein derived ACE inhibitors may be added to
food with the aim of reducing the risk of developing hypertension
[7]. Other bioactive peptides that may be sourced from food
include anticancer and antithrombotic peptides [8]. With bioac-
tive peptides showing such potential as new therapeutics,
nutraceuticals and functional food ingredients, the discovery and
prediction of new bioactive peptides is an increasingly valuable
research area.
To date, computational prediction of peptide bioactivity has
focused on antimicrobial peptides. The most recent versions of
both the antimicrobial peptide database (APD2) [9] and the
CAMP database [10] include antiviral, antifungal, antibacterial
and antiparasitic peptides. The authors have also studied the
amino acid composition of various peptide classes. The experi-
mentally validated CAMP dataset was used to develop prediction
tools based on machine learning techniques [10]. Another
predictor of antimicrobial peptides, AntiBP2 [11], based on a
Support Vector Machine (SVM) was trained on peptides from the
APD [12], using the 15 N and C terminal residues and the amino
acid composition of the whole peptide. AMPer [13], antimicrobial
peptide predictor, used hidden Markov models (HMMs) con-
PLOS ONE | www.plosone.org 1 October 2012 | Volume 7 | Issue 10 | e45012
structed from known antimicrobial peptides to discover novel
antimicrobial peptide candidates (see http://marray.cmdr.ubc.ca/
cgi-bin/amp.pl). Another new method for predicting antimicrobial
peptides was trained using sequence alignments and feature
selection [14].
A number of bioactive peptide databases which cover a range of
activities, including, but not limited to antimicrobial peptides, are
also available such as BIOPEP [8] and PeptideDB [15]. Although
there is some overlap between these databases, they are each
focused on particular classes of peptides. BIOPEP is a database of
biologically active peptide sequences, and also a tool for the
evaluation of proteins as the precursors of bioactive peptides. The
peptide activity classes found in BIOPEP include antithrombotic
peptides, antiamnestics, celiac toxins, neuropeptides, antibacterial
peptides, haemolytic, opioid, heparin binding, anticancer, immu-
nomodulating, antioxidative and peptides labelled as inhibitors,
regulating and stimulating. The PeptideDB database includes
cytokine and growth factors, peptide hormones, antimicrobial
peptides, toxin/venom peptides and antifreeze proteins. However,
there are no established prediction methods covering these classes
of peptides.
We set out to determine whether it is possible to make useful
general predictions regarding peptide bioactivity, or whether
predictions are best carried out within particular discrete sub-
classes. To assess this, we developed a general bioactive peptide
predictor, PeptideRanker, trained in five-fold cross-validation
using a novel N-to-1 Neural Network (N1-NN) [16]. This method
can prove useful to identify among a set of peptides those that are
more likely to be bioactive, allowing the focusing of experimental
screening on this subset. Our training set drew from four bioactive
peptide databases (BIOPEP, PeptideDB, APD2 and CAMP),
covering a diverse set of bioactive peptides. We then investigated
how well PeptideRanker can predict different classes of bioactive
peptides, and the impact of extracellular status and amino acid
composition on predictions.
Figure 1. ROC plots for the training and independent test datasets predicted by PeptideRanker. (A) long peptide training dataset (B)
short peptide training dataset (C) long peptide independent test dataset (D) short peptide independent test dataset. P-value%0:0001 in all four
cases.
doi:10.1371/journal.pone.0045012.g001
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Results
Training a general predictor of peptide bioactivity
In training PeptideRanker to predict bioactive peptides we
reduced over-fitting by training and testing using five-fold cross-
validation with redundancy reduced datasets and by assessing the
performance on two independent datasets. Since evolutionary
similarity among peptides in training/test and independent
datasets can contribute to over-fitting, we also investigated
whether the predictions in the independent dataset relied on
sequence similarities. Since our initial investigation indicated that
short and long peptide predictions were very different, we
developed two separate predictors, one for peptides of twenty
residues or less, and one for longer peptides.
For every peptide, PeptideRanker predicts the probability
(between 0 and 1) of that peptide being bioactive. The closer the
predicted probability is to 1, the more confident PeptideRanker is
that the peptide is bioactive. The results of five-fold cross-
validation for the training dataset, are shown in Table 1 and in
Figure 1 as a Receiver Operating Characteristic (ROC) curve with
thresholds increasing from 0 to 1, i.e. the cut-off above which a
residue is considered to be predicted as a bioactive peptide. We
also tested PeptideRanker using two independent test set of
peptides with v70% sequence similarity to the training set. Table
S1 shows the results for PeptideRanker tested on these long and
short independent test sets and Figure 1 shows the same results
plotted as ROC curves. The results for the independent test sets
(where all models of the predictor are ensembled) are slightly
better than the results in five-fold cross-validation. The predictor
for the long peptides performs better (Matthews Correlation
Coefficient (MCC) 0.74 and Area Under Curve (AUC) of 0.94)
than the predictor for the short peptides (MCC of 0.54 and AUC
of 0.83).
When machine learning methods are used to predict some
feature from protein sequences it is common to reduce the
sequence similarity within the training set and between the
training set and any test sets to v30% sequence similarity. This is
essential when, for example, predicting secondary structure from a
whole protein sequence where it is known that proteins sharing
w30% sequence similarity are often structurally similar. However,
this may not be the case with bioactive peptides, where even one
amino acid substitution can change a peptide from bioactive to
non-bioactive. Therefore, we considered that redundancy reduc-
ing to v30% sequence similarity would be too strict, and followed
previous work where sequences were redundancy reduced to
v70% sequence similarity. However, we did check for a
correlation between prediction accuracy of bioactive peptides in
the training and test sets, and the sequence similarity between the
test peptides and their most similar peptide in the training set.
Linear regression of the prediction versus the sequence similarity
indicated the percentage variance (R2) to be only 5% for the long
peptides and 1% for the short peptides. This indicates that the
predictive power of the method within the independent test dataset
is not primarily a result of sequence similarity to peptides in the
training set, but mainly arises from other features of the data. We
are therefore confident that using training datasets with v70%
sequence similarity between peptides, is appropriate, and is not
leading to homology-induced over-fitting of the model.
We estimated the relation between prediction accuracy for the
bioactive peptides and peptide length. We found little dependence
on length, with R2of 0 for the long peptides and R2of 0.01 for the
short peptides. Thus, within each predictor, the prediction is not
strongly influenced by peptide length. During initial training of the
predictors (data not shown) the datasets were not split into long
and short peptides and we did find in this case that peptide length
strongly influenced prediction accuracy which motivated us to split
the predictor into long and short peptide predictors. It is clear that
the two predictors are behaving in different ways. For example, we
found that the predictor of short bioactive peptides is more
dependent on amino acid composition than the long peptide
predictor, and we discuss this in more detail below.
Benchmarking against other predictors
We compared the predictive power of PeptideRanker against
two state-of-the-art freely available antimicrobial peptide predic-
tors, CAMP [10] and AntiBP2 [11] (Table S1). We used the
independent test sets for this comparison which share v70%
sequence similarity with the PeptideRanker training sets but had
not been redundancy reduced with respect to the CAMP or
AntiBP2 training sets. As AntiBP2 is trained on APD2 peptides [9]
and CAMP is trained on peptides from the CAMP database [10],
both of which are included in our test set, it is highly likely that at
least some of the peptide sequences in this set were used to train
CAMP and/or AntiBP2. In effect, this should give CAMP and
AntiBP2 an advantage over PeptideRanker when predicting these
peptides. However, PeptideRanker is more accurate than either
CAMP or AntiBP2 in most cases, which is not surprising as the
dataset includes peptides other than antimicrobial peptides, which
CAMP and AntiBP2 were not designed to predict.
PeptideRanker typically performed better than CAMP (on
Specificity (Spec), Sensitivity (Sen), False Positive Rate (FPR),
Accuracy (Q) and MCC) for both the long and the short peptides
(Figure 2 and Table S1). AntiBP2 had a higher sensitivity on the
control peptides and a lower FPR on the bioactive peptides than
PeptideRanker for both the long and the short peptides. However,
AntiBP2 did not return predictions for 234 of the 946 long
peptides and 392 of 532 short peptides. Therefore, AntiBP2 results
are not calculated on the whole test set. In fact AntiBP2 only
predicted 203 of the 473 true positive long peptides as bioactive,
resulting in a low sensitivity on the bioactive peptides and a high
Table 1. Performance of PeptideRanker measured in five-fold cross-validation on the training datasets at a threshold of 0.5.
Long Short
Spec Sen FPR Q MCC Spec Sen FPR Q MCC
Control 84.3 88.9 0.16 73.3 80.6 0.29
Bioactive 88.3 83.4 0.11 78.4 70.7 0.19
All 86.1 0.72 75.6 0.51
Specificity (Spec), Sensitivity (Sens) and False Positive Rate (FPR) measured for the bioactive peptide and control peptide classes. Accuracy (Q) and Matthews Correlation
Coefficient (MCC) are measured over all peptides, bioactive peptides and control peptides. See the Materials and Methods section for definitions.
doi:10.1371/journal.pone.0045012.t001
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FPR for the control set. This imbalance across the bioactive/
control peptides is reflected in a low MCC for AntiBP2 of 0.44
compared to the more balanced results for PeptideRanker of 0.74.
We see a similar pattern for the short bioactive peptides where
only 46 of the 266 bioactive peptides are predicted as bioactive,
resulting in a MCC of 0.45 compared to PeptideRanker’s MCC of
0.54. Thus, PeptideRanker is better than the antimicrobial
predictors at predicting bioactivity across all bioactive peptides.
Their relative performances within different classes, including
antimicrobial, are investigated further below.
Importance of amino acid composition
As described in the Datasets section we used two sets of protein
sequences from which we randomly selected the control peptides
i.e. ‘‘Secreted’’ and ‘‘Other’’, where ‘‘Other’’ is made up of protein
sequences not labelled as secreted or membrane, i.e. non-secreted
(see Datasets section for more details). We looked at the amino
acid composition of these two sets and the difference between
them (Table S2). The most notable difference between the two sets
is in the percentage frequency of cysteine which is 2.35% greater
in the secreted set, however cysteine is still only the 15th most
common residue in the secreted set. The other amino acids with
notable differences between the two sets are glutamic acid, lysine,
glycine and isoleucine. All other amino acids differ by less than
1%.
We then looked at the amino acid composition of the long and
the short bioactive peptides and the control peptides (secreted and
non-secreted) in the independent test sets (Figure 3) and the
differences between the bioactive peptides, the secreted and non-
secreted control peptides and the secreted and non-secreted sets
described above (Table S3). We found very little difference
between the short and long randomly selected secreted and non-
secreted control peptides. However, there were substantial
differences between the long and the short bioactive peptides,
with phenylalanine increasing in frequency from 3.9% in the long
bioactive peptides to 7.3% in the short bioactive peptides.
Similarly, glycine jumps from 7.5% to 10%, whereas glutamic
acid and threonine fall from 5.8% and 4.8% to 2.8% and 2.6%
respectively. There are also significant differences between the
amino acid composition of the bioactive peptides and the secreted
and non-secreted control peptides, again especially with the short
peptides. Glutamic acid, phenylalanine, glycine and cysteine differ
by 4.4%, 3.9%, 3.6% and 3.3% respectively between the short
bioactive peptides and the non-secreted control peptides, and
phenylalanine, glycine and glutamic acid differ by 4%, 3.5% and
2.9% between the short bioactive peptides and the secreted control
peptides. We also compared the differences between the amino
acid composition of bioactive peptides and the amino acid
composition of the full UniProt secreted protein set. The biggest
difference we found for the long bioactive peptides was a decrease
from 4.5% to 3.9% for asparagine, and an increase from 5% and
3.9% to 6.8% and 5.1% for arginine and cysteine respectively. We
observed greater changes for the short bioactive peptides with
threonine and glutamic acid decreasing by 3.3% and 3.1% and
phenylalanine and glycine increasing by 3.6% and 2.6%
respectively. Overall what is striking is the substantial composi-
tional divergence between the short and long peptide classes.
Within the two size classes, there is further variation in amino
acid frequency that relates to the peptide functional classes.
Figure 4 shows the distribution of the amino acid composition
across three broad peptide activity classes (antimicrobial, toxin/
venom, and peptide hormone). For the long peptides (Figure 4A),
cysteine stands out as the most favoured amino acid in the toxin/
venom class, whereas it is one of the least favoured in the peptide
hormone class. Leucine is the most favoured by the peptide
hormones and is also strongly favoured by the antimicrobial
peptides along with glycine, which is also favoured by the toxins
and venoms. The short peptide classes (Figure 4B) show an even
more dramatic shift in their amino acid preferences. Again, leucine
is strongly favoured by the antimicrobial peptides but not by the
toxins and venoms, which again have a very strong preference for
cysteine followed by proline. PeptideRanker may have learned
about such clustering of amino acid preferences within particular
functional classes, although the training was performed without
reference to any such classification of peptide function.
Given this striking difference in amino acid composition, we
were interested in investigating to what extent the short and long
PeptideRanker predictors make differing use of amino acid
composition, in order to identify potentially interesting rules in
the discovery and design of bioactive peptides. We inspected the
pairwise correlation of the PeptideRanker scores for the long and
the short peptides with the frequency of each amino acid (Table
S4). For a number of amino acids the correlations showed similar
Figure 2. Independent test set. Comparison of PeptideRanker (measured at a threshold of 0.5), CAMP and AntiBP2 tested on the independent test
set. (A) long peptides (B) short peptides (C) MCC for long and short peptides. AntiBP2 did not return predictions for 234 (out of a total of 946) of the
long and 392 (out of a total of 532) of the short peptides. CAMP did not return predictions for 6 of the long and 5 of the short peptides.
doi:10.1371/journal.pone.0045012.g002
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directions e.g. cysteine is markedly favoured by both predictors.
Phenylalanine was markedly favoured by the short predictor and
disfavoured by the long predictor, with a similar tendency for
proline, methionine, glycine and tryptophan; while threonine,
valine and glutamic acid were more favoured by the long predictor
(Table S4). Again, this justifies the separation into two peptide
classes and provides intriguing insights into the differences
between amino acid composition of short and long peptides.
Despite the fact that cysteine is not one of the most common
amino acids in either the bioactive peptide or the control datasets,
the number of cysteines in the peptide has the greatest correlation
with the PeptideRanker score (Table S4). To further investigate
the role of this important residue we counted the number of
sequences with an even number of cysteines in each of our protein
sequence datasets. 92% of the UniProt secreted sequences and
90% of ‘‘Other’’ (non-secreted) sequences have at least one
cysteine and of these 60% of the secreted sequences have an even
number of cysteines compared to 49% of the non-secreted
sequences. We then compared these numbers with those for the
bioactive peptides in the independent test set. Interestingly,
although the average amino acid content of the short and the
long peptides is the same (5%) only 20% of short bioactive peptides
have at least one cysteine whereas 73% of the long bioactive
peptides do. Of these sequences 73% of both sets have an even
number of cysteines. We believe that this is due to disulphide
bonding including cysteine knot formation [17]. Six cysteines,
required to form a cysteine knot, were found in 40% of the long
peptides which had an even number of cysteines but in only 23%
of the short peptides.
We then broke this down further into the three peptide activity
classes. We found that for the toxin and venom peptides 80% of
the short and all of the long peptides had at least one cysteine and
of these 89% of the short peptides and 81% of the long peptides
had an even number of cysteines, and 29% and 53% of these short
and long peptides respectively had 6 cysteines. Again, this is likely
to be due to cysteine knot formation which is known to be an
important motif in toxin peptides [17]. The numbers for the
antimicrobial peptides and the peptide hormones are not quite as
dramatic with 80%, 60%, 65% and 78% of peptides with cysteines
having an even number of cysteines for the long and short
antimicrobial peptides and peptide hormones respectively.
To investigate the use of amino acid composition by the
predictors, we scrambled the bioactive peptides of the independent
test sets, and used them as a new control set. The original bioactive
peptide set remained unchanged. We compared the ability of
PeptideRanker, AntiBP2 and CAMP to discriminate between the
bioactive peptide and the scrambled peptide. It is clear that
AntiBP2 and CAMP are mainly relying on amino acid compo-
sition, since they do not distinguish well between bioactive
peptides and their scrambled sequences (Table S5; MCC values
range between 20.01 and 0.06); this is not surprising, given that
AntiBP2 specifically relies on amino acid composition for
Figure 3. Amino acid content of the bioactive peptides, the non-secreted control peptides and the secreted control peptides in the
independent test dataset. (A) long peptides (B) short peptides.
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prediction. For PeptideRanker, the MCC is 0.11 for short and
0.34 for long peptides. This indicates that, for PeptideRanker,
amino acid composition is an important factor, but by no means
the only factor in predicting bioactivity of either short or long
peptides.
Bioactivity prediction is not simply a prediction of
extracellular localization
As most bioactive peptides are found in secreted proteins, we
selected the control set of peptides for the training dataset from
non-secreted protein sequences. This reduces the likelihood of
randomly selecting bioactive peptides for the control set by
chance. To investigate if the predictors have learned to
discriminate only between secreted and non-secreted peptides/
proteins, we created a control set of peptides composed of
randomly selected sections from a set of proteins known to be
secreted. In this situation, by selecting peptides from secreted
proteins we increased the chance of erroneously including
bioactive peptides in our control peptide sets. However, keeping
this in mind, by creating this control set of peptides from proteins
known to be secreted, we can still learn something about the
tendency to over-predict peptides as bioactive simply because they
are from secreted proteins. The results for the three predictors
(Table S6). The performance of all three methods is clearly
reduced compared to the predictive ability to distinguish bioactive
peptides from non-secreted peptides. However, the overall
performance of PeptideRanker is substantially better than either
of the other two predictors. This indicates that PeptideRanker’s
ability to predict bioactivity is not solely a matter of predicting a
peptide’s extracellular features, such as those described above, but
also relies on other properties such as the charge distribution of the
peptide or the amino acid sequence order.
Generally predictable features are shared across diverse
peptide activity classes
We investigated whether independent test set peptides of
different classes defined by PeptideDB (antimicrobial peptides,
peptide hormones and toxin/venom peptides) were differentially
predicted by the three methods. ROC curves indicate that all three
classes are successfully predicted by PeptideRanker (Figure 5).
Performance overall on the antimicrobial peptides for all three
predictors is good (Figure 6; Table S7), which is to be expected as
AntiBP2 and CAMP are specifically antimicrobial peptide
predictors, but neither of them perform well in the peptide
hormone class. Both AntiBP2 and CAMP tend to predict the
peptide hormones as non-bioactive. In fact, AntiBP2 only predicts
20 of the long and only one of the short peptide hormones as
bioactive, corresponding to a FPR (peptides incorrectly predicted
as non-bioactive) of 0.85 and 0.96 respectively (Table S8). CAMP
performs better, correctly predicting (Sensitivity) 52.9% of the long
Figure 4. Amino acid content of the three peptide activity classes from the PeptideDB.70 dataset. (A) long peptides (B) short peptides.
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peptide hormones and 35.3% of the short peptide hormones (FPR
of 0.47 and 0.65 respectively). In contrast, PeptideRanker correctly
predicts 80.7% of the long peptide hormones and 83% of the short
peptide hormones as bioactive. Interestingly, AntiBP2 and CAMP
both perform surprisingly well at predicting the toxin and venom
peptides, in both the long and short classes (Figure 6; Table S9). In
fact, in this case CAMP actually slightly outperforms PeptideR-
anker. It is important to note that AntiBP2 is unable to predict
peptides shorter than 15 amino acids in length, or longer than 100
(see Tables S7, S8 and S9), so that some differences in the
performance may be related to differences in the subset
investigated.
It can be seen in Table 2 that among short peptides the three
groups differ in their charge distributions, with antimicrobial
favouring positive charge, while the other two groups include more
peptides with overall negative charge. This is also seen for the long
peptides. It is of interest to see whether the PeptideRanker scores
predicted for short peptides are distinguishing partly on the basis
of absolute net charge, since it has been observed that short
bioactive peptides tend to favour a high absolute net charge [18].
While among the antimicrobials, the short peptide score is
positively correlated with absolute net charge (r~0:30), it is
negatively correlated for toxin/venom peptides (r~{0:19) and
peptide hormones (r~{0:30). Curiously, the scores of the short
peptide hormones are correlated with net charge (r~0:25). Thus,
charge effects may well tend to be class specific.
Finally, given that there are both common and distinguishing
features across the different classes of peptides we wished to
examine if it would be possible to predict to which of the three
peptide activity classes a bioactive peptide belonged. From the
long and short training sets, we extracted all PeptideDB peptide
sequences labelled as antimicrobial peptides, peptide hormones
and toxin/venom peptides. We trained two predictors, one for
long and one for short peptides, using the same architecture as
PeptideRanker, except using a three class output where the output
predicts the peptide activity as antimicrobial, peptide hormone or
toxin/venom. We tested whether or not it was possible for the N-
to-1 neural network to learn to discriminate between these three
classes (see Table S10). The accuracy of both predictors is very
high (85–86%). The least accurately predicted of the three classes
is the antimicrobial class, in both cases. It appears that almost all of
the false negative antimicrobial peptides are predicted to be
peptide hormones (FPR 17–21%).
Discussion
Our study identifies that bioactivity may be computationally
predicted across diverse functional classes of bioactive peptides,
and that long peptides (exceeding 20 residues) are best treated as a
Figure 5. ROC plots for long and short peptide activity classes of the PeptidesDB.70 subset of the independent test set. (A) 88 long
antimicrobial peptides (B) 243 long peptide hormones (C) 81 long toxin/venom peptides (D) 29 short antimicrobial peptides (E) 50 short peptide
hormones (F) 34 short toxin/venom peptides. P-value%0:0001 in all six cases.
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separate class, compared to short peptides. We observed that
features of the sequence of longer peptides contribute to the
predictability of peptide bioactivity, whereas in shorter peptides
the predictive power is more dominated by amino acid compo-
sition. While it is possible that such sequence effects are the result
of specific neighbourhood effects within the longer peptides (such
as preferred dipeptides or residues located at the two termini) we
consider it most likely that this corresponds to the formation of
distinct domains within the longer peptides, such as patches of
hydrophobicity or charge, representing mini-domains within the
longer peptides. The formation of such patches of properties may
not be as clearly achievable within shorter peptides.
We propose that the further evaluation of methods to refine the
prediction and design of bioactive peptides should consider short
(v20 residues) and longer peptides as distinct classes, following
distinct rules, as we have done here. This has the added advantage
that many researchers are interested in either shorter or longer
peptides, allowing the development of tools optimized towards
these different needs. Our observation that short peptides
containing phenylalanine are more likely to be predicted as
bioactive, suggests that this amino acid may be particularly useful
in conferring bioactivity to shorter peptides; and it is intriguing
that longer peptides with phenylalanine are predicted to be
bioactive less often. Since longer peptides may adopt internal
structure, this difference may indicate that phenylalanine is of
most use in forming ligand contacts (typically with larger proteins)
than in forming internal structural contacts within the peptide
itself. London et al [19] noted the strongest enrichment of
phenylalanine among peptide interface hotspot residues, consistent
with this interpretation. Thus, the two predictors may be
distinguishing among different modes of binding, those typical of
short peptides and those typical of longer peptides which have
more internal structure.
We set out in this study to develop a general predictor of
bioactivity, aiming to capture general rules that span across diverse
classes of bioactive peptides. In the course of this research, we
came to the surprising conclusion that peptide bioactivity
predictors trained in one class (antimicrobial) are equally good
at predicting toxin/venom peptides. This indicates that these two
classes of peptides may share common features, motivating the
combination of diverse peptide classes within a single predictor.
PeptideRanker provides such a predictor with good performance
across different peptide classes. It is likely that the N-to-1 neural
network is relying on rules that are general across many peptide
classes, augmented by rules that are specific to particular classes.
This may explain its superior performance over the two
antimicrobial predictors, when predicting bioactivity among
peptide hormones. What then is the power of the method to
predict peptide bioactivity for a peptide that belongs to a
functional class that is not represented within the training set?
This can only really be assessed by a formal experimental
validation, but we propose that there is sufficient general
predictive power encoded within the method to justify its use for
the prediction or optimal design of bioactive peptides belonging to
functional classes that are not represented in the training set. All
predictive methods for peptide bioactivity must be interpreted with
caution, since the predictive power is clearly not absolute. We
believe that the most useful applications will be in helping to focus
experimental decision making, permitting investigators to com-
mence analyses on peptides that are most favoured by the
software, leading to overall improved efficiency, in that fewer
inactive peptides will be screened. Users need to bear in mind,
though, that the method has false negatives as well as false
positives, and peptides which are not identified may still be
bioactive. Further biophysical characterization of the general
sequence and structural features of bioactive peptides may serve to
Figure 6. Matthews Correlation Coefficient (MCC) between
observed and predicted states for the long and short peptide
activity classes. (A) long peptides (B) short peptides.
doi:10.1371/journal.pone.0045012.g006
Table 2. Variability in charge across the three peptide activity
classes.
Mean Mean Mean absolute Absolute net
length net charge net charge
charge/
length
Long
Antimicrobial 60.8 2.9 4.1 0.09
Peptide Hormone 101.7 1.4 4.3 0.05
Toxin/Venom 48.0 1.8 3.4 0.08
Short
Antimicrobial 13.6 1.1 1.5 0.12
Peptide Hormone 10.9 0.4 1.2 0.11
Toxin/Venom 14.7 0.3 1.2 0.08
Net charge: sum of positively (K and R) and negatively (D and E) charged
residues for each peptide. Absolute net charge: modulus of net charge for each
peptide.
doi:10.1371/journal.pone.0045012.t002
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PLOS ONE | www.plosone.org 8 October 2012 | Volume 7 | Issue 10 | e45012
advance the goal of optimizing the prediction and design of
peptide bioactivity.
Materials and Methods
Datasets
Training and independent test dataset. The bioactive
peptides used to train PeptideRanker were retrieved from
PeptidesDB [15], APD2 [9], CAMP [10] and BIOPEP [8] — in
total 18,882 non-unique peptides. The PeptideDB database
includes bioactive peptides from animal sources, including
cytokine and growth factors, peptide hormones, antimicrobial
peptides, toxin/venom peptides, and antifreeze proteins. We
included any peptides labelled as peptide hormone, antimicrobial
peptide or toxin/venom peptide into the dataset. From BIOPEP
we included peptides labelled as antibacterial, antibiotic or
anticancer. We included all peptides from CAMP and APD2. A
total of 17,532 peptides.
Based on preliminary testing (not shown) we split the dataset
into long and short peptides, and trained two independent
predictors. We chose a threshold of 20 amino acids based on
our subjective idea of what would be considered a short as opposed
to a long peptide. 20 amino acids is also the length of the shortest
known protein with a stable fold [20]. Splitting the peptides into
long (w20AA) and short (4–20AA) peptide sets left 13,775 and
3,728 peptides in the long and short sets respectively. We then
independently internally redundancy reduced both sets to v70%
sequence similarity using BLAST [21] to create the alignments. As
we were searching for short and nearly exact matches we set the
word-sizes to 2 (-W 2), low-complexity filter to off (-F F), we set the
e-value to 20000 (-e 20000) and used the PAM30 substitution
matrix (-M PAM30). This left 4,731 peptides in the long set and
1,330 peptides in the short set.
These two peptide sets were then split into two further sets. One
fifth of the short peptides and one tenth of the long peptides were
reserved as independent test sets and the remaining peptides
became the training set. The training set was then split into five
folds i.e. five different sets of peptides where each set is roughly
equally sized, disjoint, and their union covers the whole set.
Similar to AntiBP2 [11] the control test set was generated by
matching each bioactive peptide with a random peptide from a
eukaryotic protein sequence which is known to be intracellular.
From the UniProtKB/Swiss-Prot Release 2011_02 we retrieved
all eukaryotic Swiss-Prot sequences [22]. We searched these
sequences extracting any sequence with a subcellular location
annotation, excluding any non-experimental annotations (i.e.
those with key words ‘‘by similarity’’, ‘‘probable’’ or ‘‘potential’’).
We divided these sequences into three sets: ‘‘Secreted’’, ‘‘Mem-
brane’’ and ‘‘Other’’. For every peptide in our bioactive set, we
randomly selected a sequence from the ‘‘Other’’ set, and then
randomly selected a starting position and extracted a peptide of
equal length to the bioactive peptide. We then checked if this
peptide was found in the list of known bioactive peptides (i.e. the
set of 18,882 non-unique peptides). If it was not found we kept this
peptide and added it to our control set, otherwise searching again.
In this way we have tried to reduce the possibility of including
bioactive peptides in our control set, however it is possible that
some of our control peptides may be bioactive as they have not
been experimentally determined to be non-bioactive. See Table
S11 for the number of peptides and average length of peptides in
each dataset and Figure S1 for histograms of the length
distributions of the four datasets.
In our testing of PeptideRanker we created two further control
test sets. In the first case, for all peptides in the independent test
set, we generated the corresponding control peptides by scram-
bling the amino acids of the known bioactive peptides. For the
second set, we generated the corresponding control peptides as
described above, except that the control peptides were extracted
from protein sequences in the ‘‘Secreted’’ set of sequences.
Peptide activity classes. From the long and short indepen-
dent test sets, we extracted all PeptideDB peptide sequences. We
refer to this set as the PeptideDB.70 dataset, as no peptide
sequence in this dataset has w70% sequence similarity to any
peptide in the training set. We divided these peptides into three
activity subsets i.e. antimicrobial peptides, peptide hormones and
toxin/venom peptides (see Table S12). We used the three activity
subsets to compare the predictive power of PeptideRanker and
two other state-of-the-art antimicrobial peptide predictors in each
of these three bioactive peptide classes.
Predictive architecture: N1-NN
N-to-1 Neural Networks, or N1-NN, have been successfully
used to predict the subcellular location of protein sequences [16].
Here, we apply this model to the prediction of peptide bioactivity.
The aim of the model is to map a peptide sequence of variable
length Ninto a single property i.e. bioactive or non-bioactive.
Other models tackle this problem at the source, that is, they
transform/compress the sequence into a fixed number of
descriptors (or into descriptors of pairwise relations between
sequences) beforehand, and then map these descriptors into the
property of interest. These descriptors are typically frequencies of
residues or k-mers, sometimes computed separately on different
parts of the sequence [11,23]. In N1-NN we do not compress the
peptide in advance, instead, we decide beforehand only how many
features we want to compress a peptide into. These features are
stored in a vector f~(f1,...,fh), we represent the i-th residue in
the peptide as ri, and then fis obtained as:
f~kX
N
i~1
N(h)(ri{c,...,rizc)ð1Þ
where N(h)is a non-linear function, which we implement with a
two-layered feed-forward Neural Network with hnon-linear
output units (the peptide-to-feature network). N(h)is replicated
Ntimes (Nbeing the peptide length), and kis a normalization
constant. The feature vector fis obtained by combining
information coming from all windows of 2cz1residues in the
peptide. In this work the windows have a length of 21 residues for
the short peptide predictor i.e. covering the length of the longest
peptide, and 41 for the long peptide predictor (see Table S13). The
feature vector fis mapped into the property of interest o(i.e.
bioactive/non-bioactive peptide), as follows:
o~N(o)(f)ð2Þ
where N(o)is a non-linear function which we implement with a
second two-layered feed-forward neural network (the feature-to-
output network). The whole compound neural network (the
cascade of Nreplicas of the peptide-to-feature vector network and
one feature-to-output network) is itself a feed-forward neural
network and thus can be trained by gradient descent via the back-
propagation algorithm. As there are Ncopies of N(h)for a peptide
of length N, there will be Ncontributions to the gradient for this
network, which are simply added together. See [16] for more
details on N-to-1 Neural Networks.
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Training. For each training experiment we implemented two
predictors, one for long (w20 amino acids) and one for short
bioactive peptides. Each training was conducted in five-fold cross-
validation, i.e. five different sets of training runs were performed in
which a different fifth of the overall set was reserved for testing and
another fifth was reserved for validating. The five fifths were
roughly equally sized, disjoint, and their union covered the whole
set. The training set was used to learn the free parameters of the
network by gradient descent, while the validation set was used to
monitor the training process. For each different architecture we
ran three trainings (V0, V1, V2), which differed in the number of
hidden units in both the peptide-to-feature and the feature-to-
output networks, and size of the feature vector (i.e. the number of
output units in the peptide-to-feature network) (see Table S13).
This ensured that the resulting models were different, which yields
larger gains when ensembled.
The weights in the networks were updated every 150 examples
(peptides) for the short bioactive peptide predictor and every 500
examples for the long bioactive peptide predictor. 500 epochs of
training were performed, which brought the training error to near
zero in all cases. Training was performed by gradient descent on
the error, which we modelled as the relative entropy between the
target class and the output of the network. The overall output of
the network (output layer of N(o)()) was implemented as a softmax
function, while all internal squashing functions were implemented
as hyperbolic tangents. The examples were shuffled between
epochs. We used a momentum term of 0:9— although this did not
significantly affect the final result, it sped up the overall training
time by a factor of 10. The learning rate was kept fixed at 0:2
throughout training.
During training the networks that performed best on the
validation set were saved, these models were then averaged over
the ensemble and evaluated on the corresponding test set. The
final results for the five-fold cross-validation are the average of the
results on each test set. When testing on an entirely different set
from the one used during training (i.e. the independent test set, see
the Datasets Section) we ensemble-combined all the models from
all cross-validation folds of the best architecture.
Evaluating performance
To evaluate the performance of PeptideRanker we measured
Specificity (Spec), Sensitivity (Sens), the True Positive Rate (TPR),
the False Positive Rate (FPR), Matthews Correlation Coefficient
(MCC) and the Accuracy (Q) as follows (see [24] for more details):
Spec~100 TP
TPzFP
Sens~100 TP
TPzFN
TPR~TP
TPzFN
FPR~FP
FPzTN
MCC~TP|TN {FP|FN
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
(TPzFP)(TPzFN)(TN zFP)(TN zFN )
p
Q~100 TPzTN
TPzTNzFPzFN
where:
NTrue Positives (TP): the number of peptides predicted in a class
that are observed in that class
NFalse Positives (FP): the number of peptides predicted in a class
that are not observed in that class
NTrue Negatives (TN): the number of peptides predicted not to
be in a class that are not observed in that class
NFalse Negatives (FN): the number of peptides predicted not to
be in a class that are observed in that class
We measure the TPR and FPR as we increase the discrimina-
tion threshold from 0 to 1. The results are shown as Receiver
Operating Characteristic (ROC) curves where TPR is plotted
against FPR. The area under the curve (AUC), which is equivalent
to the probability that the classifier will rank a randomly chosen
positive instance higher than a randomly chosen negative instance
[25], is also shown. The AUC is a number between 0 and 1
inclusive, where 0.5 indicates a random model and 1 is perfect. We
used the ‘‘verification’’ package in R [26] to plot the ROC curves
and calculate the AUC and to plot the linear regressions from
which we derived the correlation, r, and variance, R2i.e. how well
future prediction are likely to be predicted by the model. MCC
measures the correlation coefficient between the observed and
predicted classifications. A value of 1 represents a perfect
prediction, 0 a random prediction and {1an inverse prediction
and is a good indicator of the overall performance of the predictive
methods in both bioactive peptide and control peptide classes.
Implementation
PeptideRanker has been implemented as a web server. The user
may submit a list of peptide sequence and PeptideRanker will
predict the probability that each of these peptides will be bioactive
or not. The list will then be returned to the user ranked by the
predicted probability of bioactivity for each peptide. The web
server input and output pages are shown in Figure S2 and S3.
When the user is interpreting the results is important to note that
the server predicts how likely the peptide is to be bioactive, and not
how bioactive the peptide is likely to be.
We have chosen as an example the Bovine milk protein beta-
casein (UniProt accession number P02666). Beta-casomorphins,
which originate from beta-casein, are a group of bioactive peptides
[27]. Beta-casomorphin 7 (YPFPGPI) is possibly the most
important of these and may have a potential role in human
disease such as ischaemic heart disease, diabetes mellitus, sudden
infant death syndrome, autism and schizophrenia [27]. This
peptide was not included in our training or test sets. We illustrate
use of the predictor by examining a series of 7-mer peptides
spanning the beta-casomorphin 7 region (Figure S2) and
submitted these to PeptideRanker (Figure S3). To run this
example on the server click on ‘‘example’’ and then click the
‘‘submit’’ button. Only one of the overlapping peptides scored
slightly higher, and it overlaps for 6 of its 7 residues. Many of the
overlapping peptides are predicted not to be bioactive, since they
are returned with values of under 0.5. (Figure S3). Human beta-
casomorphin 7 (YPFVEPI) differs in two positions to the Bovine
peptide and is not predicted as strongly to be bioactive (0.506).
The other Bovine beta-casomorphins (4, 5, 6, 8 and 11) are also
predicted to be bioactive (0.828–0.969). Note that the web server
automatically uses the short peptide predictor for peptides of less
Discovery and Design of Functional Peptides
PLOS ONE | www.plosone.org 10 October 2012 | Volume 7 | Issue 10 | e45012
than 20 amino acids, and the long peptide predictor for peptides of
20 or more residues.
PeptideRanker was trained at a threshold of 0.5 i.e. any peptide
predicted over a 0.5 threshold is labelled as bioactive. However,
the user may decide to chose a higher threshold to reduce the
number of false positives. From our testing (Figure 1) we would
expect that choosing a threshold of 0.8 will reduce the false
positive rate from 11% and 16% at a 0.5 threshold to 2% and 6%
at a 0.8 threshold for long and short peptides respectively.
However, increasing the threshold to 0.8 from 0.5 also reduces the
true positive rate so the user needs to chose a threshold carefully
based on their needs i.e. is it more important to reduce the number
of false positives or capture all the true positives?
Another factor that will reduce the number of true positives
predicted by PeptideRanker is if the peptide has a cysteine content
lower than that of most secreted proteins/peptides i.e. v4%.
When we tested PeptideRanker we observed that the false
negatives in our independent test set (bioactive peptides incorrectly
predicted as non-bioactive) had an average cysteine content of just
1.7%.
PeptideRanker was trained and tested on amino acid sequences
without any modifications. Peptide synthesis using the 20 natural
amino acids has the benefit of drawing on the same set of features
as natural proteins and are often relatively inexpensive to
synthesise. However, peptide modifications such as the acetylation
of the N-terminus, the amidation of the C-terminus, the cyclisation
of the peptide or the inclusion of one or more D-amino acids may
increase the bioactivity and the specificity of the peptide [28]. The
inclusion of D-amino acids may also increase protease resistance
[29]. We would suggest that after using PeptideRanker to discover
bioactive peptides in Silico that various peptide modification would
be experimented with in Vitro to increase the bioactivity.
PeptideRanker is free for academic use and available at http://
bioware.ucd.ie/. The datasets used to train PeptideRanker are
available on request.
Supporting Information
Figure S1 Histograms of peptide length distribution. (A)
long peptide training set (B) short peptide training set (C) long
peptide test set (D) short peptide test set.
(EPS)
Figure S2 Web server sequence input page.
(PNG)
Figure S3 Web server results page.
(PNG)
Table S1 Independent test set with control peptide set
selected from non-secreted proteins. Comparison of
PeptideRanker (measured at a threshold of 0.5), CAMP and
AntiBP2 tested on the independent test set. AntiBP2 did not return
predictions for 234 (out of a total of 946) of the long and 392 (out
of a total of 532) of the short peptides. CAMP did not return
predictions for 6 of the long and 5 of the short peptides.
(PDF)
Table S2 Percentage amino acid composition of Uni-
Prot secreted and non-secreted proteins.
(PDF)
Table S3 Difference in amino acid composition be-
tween datasets. Percentage differences in amino acid content
between the differente datasets. The first seven columns and the
last column are absolute values.
(PDF)
Table S4 Pairwise correlations between amino acid
composition and PeptideRanker scores. Pearson correla-
tion coefficient for long and short peptides between amino acid
composition and PeptideRanker scores, and the difference (Diff)
between long and short correlations.
(PDF)
Table S5 Independent test set with a control peptide set
of scrambled bioactive peptides. Comparison of PeptideR-
anker (measured at a threshold of 0.5), CAMP and AntiBP2 tested
on the independent test set. The control peptides are generated by
scrambling the bioactive peptide set i.e. the amino acid
composition of both the control and the bioactive set is the same.
AntiBP2 did not return predictions for 234 of the long and 393 of
the short peptides. CAMP did not return predictions for 12 of the
long and 8 of the short peptides.
(PDF)
Table S6 Independent test set with control peptide set
from secreted proteins. Comparison of PeptideRanker
(measured at a threshold of 0.5), CAMP and AntiBP2 tested on
the independent test set, with control peptides randomly selected
from secreted proteins. AntiBP2 did not return predictions for 223
of the long and 394 of the short peptides. CAMP did not return
predictions for 11 of the long and 7 of the short peptides.
(PDF)
Table S7 Prediction of antimicrobial peptides. Compar-
ison of AntiBP2, CAMP and PeptideRanker tested on the
PeptideDB.70 Antimicrobial peptide activity class subset of the
independent test set. AntiBP2 did not return predictions for 20 of
the 176 long and 34 of the 58 short peptides. CAMP did not return a
prediction for one of the long peptides. Statistics were calculated on
the subset of peptides for which predictions were available.
(PDF)
Table S8 Prediction of peptide hormones. Comparison of
AntiBP2, CAMP and PeptideRanker tested on the PeptideDB.70
Peptide hormone activity class subset of the independent test set.
AntiBP2 did not return predictions for 212 of the 486 long and
254 of the 300 short peptides. CAMP did not return predictions
for five of the long peptides. Statistics were calculated on the subset
of peptides for which predictions were available.
(PDF)
Table S9 Prediction of toxin/venom peptides. Compar-
ison of AntiBP2, CAMP and PeptideRanker tested on the
PeptideDB.70 Toxin/Venom peptide activity class subset of the
independent test set. AntiBP2 did not return predictions for 38 of
the 68 short peptides. CAMP did not return a prediction for one of
the short peptides. Statistics were calculated on the subset of
peptides for which predictions were available.
(PDF)
Table S10 Performance of three class peptide activity
predictor measured in five-fold cross-validation on the
peptide activity subsets of the full training datasets at a
threshold of 0.5. The number of peptides per class (Num),
Specificity (Spec), Sensitivity (Sens), Matthews Correlation Coef-
ficient (MCC) and False Positive Rate (FPR) measured for the
three peptide activity classes. Accuracy (Q) and Generalised
Correlation (GC) are measured over all peptides. See the Materials
and Methods section for definitions.
(PDF)
Table S11 Number of peptides and average peptide
length per class.
(PDF)
Discovery and Design of Functional Peptides
PLOS ONE | www.plosone.org 11 October 2012 | Volume 7 | Issue 10 | e45012
Table S12 Number of bioactive and control peptides
per activity class. Number of bioactive and control peptides per
activity class in the PeptideDB.70 subset of the independent test
sets (see Figures 4 and 5).
(PDF)
Table S13 Network parameters. Nf: size of the feature
vector; NH
o: number of hidden units in the feature-to-output
network; NH
f: number of hidden units in the peptide-to-feature
network; cis the context i.e. 2cz1is the size of the window being
considered.
(PDF)
Acknowledgments
The authors acknowledge the Research IT Service at University College
Dublin for providing high performance computing (HPC) resources that
have contributed to the research results reported within this paper.
Author Contributions
Conceived and designed the experiments: CM GP DCS. Performed the
experiments: CM. Analyzed the data: CM DCS. Wrote the paper: CM GP
DCS. Implemented webserver: CM NJH.
References
1. Alanis A (2005) Resistance to antibiotics: are we in the post-antibiotic era? Arch
Med Res 36: 697–705.
2. Hancock R, Sahl H (2006) Antimicrobial and host-defense peptides as new anti-
infective therapeutic strategies. Nat Biotechnol 24: 1551–1557.
3. Fjell C, Hiss J, Hancock R, Schneider G (2012) Designing antimicrobial
peptides: form follows function. Nat Rev Drug Discov 11: 37–51.
4. Edwards R, Moran N, Devocelle M, Kiernan A, Meade G, et al. (2007)
Bioinformatic discovery of novel bioactive peptides. Nat Chem Biol 3: 108–112.
5. Hubbell J (1999) Bioactive biomaterials. Curr Opin in Biotech 10: 123–129.
6. Demidova-Rice T, Geevarghese A, Herman I (2011) Bioactive peptides derived
from vascular endothelial cell extracellular matrices promote microvascular
morphogenesis and wound healing in vitro. Wound Repair Regen 19: 59–70.
7. FitzGerald R, Meisel H (2000) Milk protein-derived peptide inhibitors of
angiotensin-I-converting enzyme. Brit Jour Nutr 84: 33–37.
8. Dziuba J, Minkiewicz P, Nałecz D, Iwaniak A (1999) Database of biologically
active peptide sequences. Nahrung 43: 190–195.
9. Wang G, Li X, Wang Z (2009) APD2: the updated antimicrobial peptide
database and its application in peptide design. Nucleic Acids Res 37: D933.
10. Thomas S, Karnik S, Barai R, Jayaraman V, Idicula-Thomas S (2010) CAMP: a
useful resource for research on antimicrobial peptides. Nucleic Acids Res 38:
D774.
11. Lata S, Mishra N, Raghava G (2010) AntiBP2: improved version of antibacterial
peptide prediction. BMC Bioinformatics 11: S19.
12. Wang Z, Wang G (2004) APD: the antimicrobial peptide database. Nucleic
Acids Research 32: D590–D592.
13. Fjell C, Hancock R, Cherkasov A (2007) AMPer: a database and an automated
discovery tool for antimicrobial peptides. Bioinformatics 23: 1148–1155.
14. Wang P, Hu L, Liu G, Jiang N, Chen X, et al. (2011) Prediction of antimicrobial
peptides based on sequence alignment and feature selection methods. PLoS
ONE 6: e18476.
15. Liu F, Baggerman G, Schoofs L, Wets G (2008) The construction of a bioactive
peptide database in metazoa. J Proteome Res 7: 4119–4131.
16. Mooney C, Wang Y, Pollastri G (2011) SCLpred: protein subcellular localization
prediction by N-to-1 neural networks. Bioinformatics 27: 2812–2819.
17. Craik D, Daly N, Waine C (2001) The cystine knot motif in toxins and
implications for drug design. Toxicon 39: 43–60.
18. Parthasarathi L, Casey F, Stein A, Aloy P, Shields D (2008) Approved drug
mimics of short peptide ligands from protein interaction motifs. J Chem Inf
Model 48: 1943–1948.
19. London N, Movshovitz-Attias D, Schueler-Furman O (2010) The structural
basis of peptide-protein binding strategies. Structure 18: 188–199.
20. Neidigh J, Fesinmeyer R, Andersen N (2002) Designing a 20-residue protein.
Nature Structural & Molecular Biology 9: 425–430.
21. Altschul S, Madden T, Scha¨ffer A, Zhang J, Zhang Z, et al. (1997) Gapped
BLAST and PSIBLAST: a new generation of protein database search programs.
Nucleic Acids Res 25: 3389–3402.
22. Boeckmann B, Bairoch A, Apweiler R, Blatter M, Estreicher A, et al. (2003) The
Swiss-Prot protein knowledgebase and its supplement TrEMBL in 2003. Nucleic
Acids Res 31: 365–370.
23. Lata S, Sharma B, Raghava G (2007) Analysis and prediction of antibacterial
peptides. BMC bioinformatics 8: 263.
24. Baldi P, Brunak S, Chauvin Y, Andersen C, Nielsen H (2000) Assessing the
accuracy of prediction algorithms for classification: an overview. Bioinformatics
16: 412–424.
25. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:
861–874.
26. R Development Core Team (2008) R: A Language and Environment for
Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
URL http://www.R-project.org. ISBN 3-900051-07-0.
27. Kamin
´ski S, Cies
´lin
´ska A, Kostyra E (2007) Polymorphism of bovine beta-casein
and its potential effect on human health. Journal of applied genetics 48: 189–
198.
28. Hruby V (2002) Designing peptide receptor agonists and antagonists. Nature
Reviews Drug Discovery 1: 847–858.
29. Fischer P (2003) The design, synthesis and application of stereochemical and
directional peptide isomers: a critical review. Current Protein and Peptide
Science 4: 339–356.
Discovery and Design of Functional Peptides
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