Conceived and designed the experiments: WSS SMB KOW. Performed the experiments: WSS CIJ. Analyzed the data: WSS SMB SCB KOW. Contributed reagents/materials/analysis tools: WSS CIJ SMB SCB KOW. Wrote the paper: WSS SMB. Obtained permission for use of cell line: SMB SCB.
The authors have declared that no competing interests exist.
Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.
Cell penetrating peptides (CPPs) are peptides that can potentially transport other functional molecules across cellular membranes and therefore serve a role as drug delivery vehicles. The properties of a given peptide that make it cell penetrating are unclear, and the rapid screening of potential CPPs aids researchers by allowing focus on those peptides most likely to be utilized in a therapeutic capacity. This paper shows that basic features representing primary biochemical properties of these peptides can be used to train a classifier that can accurately predict cell penetrating potential of peptides and provide insight into the biochemical properties associated with cell penetration.
Cell penetrating peptides (CPPs), also referred to as “Trojan” peptides, protein transduction domains, or membrane translocation sequences, are typically hydrophobic linear arrangements of 8–24 amino acids able to cross the lipid bi-layer membrane that serves as the cell’s outer barrier and gain access to the interior of the cell and its components
Initially, cellular uptake of CPPs was believed to be through endocytosis or protein transporters, but some evidence suggested the mechanism may involve direct transport through the lipid bi-layer of the cell, which takes into account the hydrophobic properties of most of these peptides
Cell penetrating peptides capable of transporting other active molecules inside the cell have the potential to serve as drug delivery peptides. Drug delivery peptides and CPPs allow researchers to probe the mechanisms of peptide transport across a lipid bi-layer membrane and may allow customizable drug therapies for differing types of cells. Although there is some controversy regarding CPPs as drug delivery systems because of their lack of specificity for cell type, the general consensus among researchers is that both general CPPs and cell-specific CPPs will be developed into effective drug delivery systems in the future
A classification system that can determine whether or not a unique peptide sequence can serve as a CPP, and thus possibly be a potential drug delivery peptide, can enable researchers to quickly screen candidate molecules for their potential viability for use in a customizable drug delivery regime.
Much of the previous work in the prediction of CPPs has involved the use of a set of composite features assembled from primary biochemical properties through the use of principal component analysis
Using the basic biochemical properties of peptides as features instead of the widely used composite
The goal of this study was to develop a machine learning approach for rapid screening of potential CPPs. We use features representing primary biochemical properties directly rather than using a transformation such as PCA that combines multiple features into a single composite feature as reported by others
Because of the sensitivity of classifiers to unbalanced classes
We designed an experiment to investigate the effect of unbalanced datasets on CPP prediction and to find methods to address the problem to evaluate classifier accuracy with precision. For the CPP prediction problem, there are many more positive examples than negative examples available. Five different approaches were used to generate training datasets for investigating this issue:
The performance of all classifiers on the training data sets is based on 10-fold cross validation. The confusion matrices for classifiers trained using datasets based on approaches 1–4 are shown in
Non-CPP | CPP | ←Classified as | |
Dataset 1 – Unbalanced. | |||
(total examples 145) | 0 | 34 | Non-CPP |
1 | 110 | CPP |
Dataset 2 – Balanced with random peptides as negatives. | |||
10-fold cross validation with training data (total examples 222) | 109 | 2 | Non-CPP |
7 | 104 | CPP | |
Tested on unbalanced data (total examples 145) | 12 | 22 | Non-CPP |
6 | 105 | CPP |
Dataset 3 – Balanced with biological peptides as negatives. | |||
10-fold cross validation with training data (total examples 222) | 108 | 3 | Non-CPP |
10 | 101 | CPP | |
Tested on unbalanced data(total examples 145) | 10 | 24 | Non-CPP |
6 | 105 | CPP |
Dataset 4 – Balanced by sampling known negatives. | |||
10-fold cross validation with training data (total examples 222) | 96 | 15 | Non-CPP |
10 | 101 | CPP | |
Tested on unbalanced data (total examples 145) | 29 | 5 | Non-CPP |
7 | 104 | CPP |
Unbalanced | Balanced with random negatives | Balanced with biological negatives | Balanced by sampling from known negatives | Balanced by sampling from known positives |
|
Accuracy | 75.86% | 95.94% | 94.14% | 88.73% | 78.82% |
True Positive Rate | 0.759 | 0.959 | 0.941 | 0.887 | 0.7883 |
False Positive Rate | 0.768 | 0.041 | 0.059 | 0.113 | 0.2117 |
ROC | 0.495 | 0.959 | 0.941 | 0.887 | 0.7883 |
*- These values represent the averages for 10 datasets.
Unbalanced | Balanced with random negatives | Balanced with biological negatives | Balanced by sampling from known negatives | |
Accuracy | 75.86% | 80.69% | 79.31% | 91.70% |
True Positive Rate | 0.759 | 0.807 | 0.793 | 0.917 |
False Positive Rate | 0.768 | 0.508 | 0.553 | 0.127 |
ROC | 0.495 | 0.649 | 0.620 | 0.895 |
The classifiers trained using both the dataset balanced with random peptides for negatives (approach 2) and with biological peptides for negatives (approach 3) had classification accuracies of 95.95% and 94.14% respectively, indicating that both classifiers exhibit a high degree of accuracy in discriminating between known cell-penetrating peptides and randomly generated or biological peptides assumed to be negative. The confusion tables for these classifiers on the training data sets (
The classifier trained on the data set constructed using approach 4 (random sampling with replacement from the known negative examples) has a classification accuracy of 88.74% on the training data set when evaluated with 10-fold cross validation. When compared to the classification accuracy of the dataset generated using the unbalanced dataset, these results show that it is possible to classify a set of CPPs and a set of known non-penetrating peptides using our SVM based method when care is used to construct balanced datasets.
Approach 2 using randomly selected biological peptides as the negative examples gives the best 10-fold cross validation accuracy while approach 4 with random selection from the negative examples gives the best accuracy for the unbalanced training set. This suggests use of a two step process for screening. In the first step, a classifier trained with random biological peptides as the negative examples would be used for preliminary bulk screening. As a second step, peptides predicted to be CPP in step 1 would be screened by a classifier trained using approach 4 that is more accurate in distinguishing non-penetrating analogs from CPPs. Approach 4 also provides more insight into the rational design of novel CPP analogs as the negative examples used in this approach are generally constructed by the modification of a known CPP sequence.
In Hällbrink et al. (2005), the authors describe a method of CPP prediction based on scoring a candidate peptide according to
Hällbrink-2005 |
Hansen-2008 |
Dobchev-2010 |
Unbalanced | Distribution-based | Biologically-based | Balanced by sampling Non-CPPs | |
Overall Accuracy | 77.27% | 67.44% | 83.16% | 75.86% | 80.69% | 79.31% | 91.72% |
CPP Accuracy | 88.46% | 80.30% | 92.21% | 99.10% | 94.59% | 94.59% | 93.69% |
Non-CPP Accuracy | 35.71% | 25.00% | 54.17% | 0.00% | 35.29% | 29.41% | 85.29% |
For each classifier constructed, feature selection was conducted using a scatter search approach through feature space
Dataset 1(Balanced with random negative examples) | Dataset 2(Balanced with biological peptides assumed to be negative) | Dataset 3(Unbalanced dataset) | Dataset 4(Balanced by random sampling of known negatives with replacement) |
Net Charge | Net Charge | Net Charge | Negative Charge |
Positive Charge | Isoelectric Point | Positive Charge | Isoelectric Point |
Number of serines (S) | Molecular Weight | Number of alanines (A) | Number of glycines (G) |
Number of aspartates (D) | Hydropathicity | Number of arginines (R) | Number of alanines (A) |
Percent valine (V) | Number of valines (V) | Percent arginines (R) | Number of tryptophans (W) |
Percent proline (P) | Number of lysines (K) | Net Donated Hydrogen Bonds | Number of asparagines (N) |
Percent phenylalanine (F) | Number of arginines (R) | Number of lysines (K) | |
Percent threonine (T) | Percent glycine (G) | Number of histidines (H) | |
Percent asparagine (N) | Percent methionine (M) | Number of aspartates (D) | |
Percent tyrosine (Y) | Percent tyrosine (Y) | Percent phenylalanine (F) | |
Percent cysteine (C) | Percent cysteine (C) | Percent tryptophan (W) | |
Percent arginine (R) | Percent aspartate (D) | Percent arginine (R) | |
Percent histidine (H) | Percent negative | Percent histidine (H) | |
Percent aspartate (D) | Water Octanol Partition Coefficient | Percent Hydrophobic | |
Percent negative | Net Donated Hydrogen Bonds | Percent negative | |
Steric Bulk | Percent Helix | Hydrophobicity | |
Net Donated Hydrogen Bonds | Percent Coil | Water Octanol Partition Coefficient | |
Percent Helix | |||
Percent Coil |
Dataset 1 | Dataset 2 | Dataset 3 | Dataset 4 | Dataset 5 | Dataset 6 | Dataset 7 | Dataset 8 | Dataset 9 | Dataset 10 |
Number (V) | Length | Number (R) | Net Charge | Net Charge | Percent (T) | Net Charge | Positive Charge | Number (W) | Positive Charge |
Percent (R) | Net Charge | Percent (W) | Negative Charge | Percent (I) | Percent (Y) | Positive Charge | Number (G) | Number (T) | Percent (I) |
Number (V) | Percent positive | Number (I) | Hydrophobicity | Net Donated Hydrogen Bonds | Percent (I) | Number (S) | Number (R) | Amphipacity | |
Number (C) | Amphipacity | Number (H) | Net Donated Hydrogen Bonds | Percent Sheet | Percent (W) | Percent (F) | Percent (S) | ||
Percent (H) | Percent Helix | Percent (F) | Percent Hydrophobic | Percent (R) | Percent (T) | ||||
Net Donated Hydrogen Bonds | Net Donated Hydrogen Bonds | Percent (H) | |||||||
Amphipacity |
Balanced subsets of CPPs sampled with replacement combined with known-CPP analogs.
To experimentally validate our feature selection methodology and classifiers, 250 random peptides were generated using a 0th order Markov model based on the chicken predicted proteome and were classified as penetrating or non-penetrating using the classifier trained on the dataset constructed using random peptides as negative examples. From these classifications, four peptides predicted to be cell-penetrating and two peptides predicted to be non-penetrating were selected for synthesis and FITC-labeling along with three known cell penetrating peptides used for positive controls, three peptides consisting respectively of only polar amino acids, only non-polar amino acids, and only of mixed polar and non-polar amino acids to serve as negative controls. In addition, a known non-penetrating peptide (TP13, a transportan analog
The uptake of synthesized FITC-labeled peptides was examined using an avian system to validate both our wrapper based feature selection methodology and SVM-based approach to predicting CPPs. The results of our fluorescence microscopy analysis are shown in
To evaluate the relative uptake of our synthesized peptides and to provide a secondary confirmation of the fluorescence microscopy results, a quantitative uptake study was conducted using both quail SOgE cells and chicken embryonic fibroblasts. The results of the quantitative uptake study for those peptides shown to be penetrating (p≤0.05) are shown in
TP13 was chosen as a non-penetrating CPP analog based on its non-CPP classification in a study examining the effects of deletion on a known CPP, transportan (TP)
Peptide-6 (HSPIIPLGTRFVCHGVT) was predicted to be a non-CPP by our classifier, but was shown to internalize into both SOgE and CEF cells experimentally both by fluorescence microscopy and the quantitative uptake studies. This peptide contains 3 positively charged amino acids along with phenylalanine. The Sommets,
Our research shows that using the primary biochemical properties of peptides as features instead of composite features determined through the use of PCA can provide both more informative features and higher classification accuracies when using support vector machines for the classification of a given peptide as cell-penetrating. The lack of a comprehensive and coherent database of cell-penetrating peptide data for bioinformatics analysis has been noted previously
A database of cell-penetrating peptides was constructed from the literature and from commercial vendor product lines
Cell-penetrating peptide | Reference |
AAVALLPAVLLALLAKNNLKDCGLF |
|
AAVALLPAVLLALLAKNNLKECGLY |
|
AAVALLPAVLLALLAPVQRKQKLMP |
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AAVALLPAVLLALLAVTDQLGEDFFAVDLEAFLQEFGLLPEKE |
|
AAVLLPVLLAAP | |
AGYLLGKINLKALAALAKKIL | |
AGYLLGKLKALAALAKKIL |
|
AHALCLTERQIKIWFQNRRMKWKKEN |
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AHALCPPERQIKIWFQNRRMKWKKEN |
|
ALWKTLLKKVLKA |
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AYALCLTERQIKIWFANRRMKWKKEN |
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CGPGSDDEAAADAQHAAPPKKKRKVGY |
|
CNGRC |
|
CNGRCG |
|
CNGRCGGKKLKLLKLL |
|
CNGRCGGKLAKLAKLAKLAK |
|
CNGRCGGLVTT |
|
GAARVTSWLGRQLRIAGKRLEGRSK |
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GALFLGFLGAAGSTMGAWSQPKSKRKV |
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GGRQIKIWFQNRRMKWKK |
|
GIGKFLHSAKKWGKAFVGQIMNC |
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GLAFLGFLGAAGSTMGAWSQPKSKRKV |
|
GRKKRRQ |
|
GRKKRRQRRPPQC |
|
GRKKRRQRRRC | |
GRKKRRQRRRPPC | |
GRKKRRQRRRPQ | |
GRQLRIAGKRLEGRSK |
|
GWTLNPAGYLLGKINLKALAALAKKIL | |
GWTLNPPGYLLGKINLKALAALAKKIL | |
GWTLNSAGYLLGKINLKALAALAKKIL | |
GWTLNSAGYLLGKINLKALAALAKKLL | |
GWTLNSAGYLLGKLKALAALAKKIL | |
GWTLNSKINLKALAALAKKIL |
|
INLKALAALAKKIL |
|
IWFQNRRMKWKK |
|
KALAALLKKWAKLLAALK |
|
KALAKALAKLWKALAKAA | |
KALKKLLAKWAAAKALL | |
KCRKKKRRQRRRKKLSECLKRIGDELDS |
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KCRKKKRRQRRRKKPVVHLTLRQAGDDFSR |
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KETWWETWWTEWSQPKKKRKV |
|
KETWWETWWTEWSQPKKRKV |
|
KFHTFPQTAIGVGAP |
|
KITLKLAIKAWKLALKAA | |
KIWFQNRRMKWKK |
|
KLAAALLKKWKKLAAALL | |
KLALKALKALKAALKLA | |
KLALKLALKALKAALK | |
KLALKLALKALQAALQLA |
|
KLALKLALKAWKAALKLA | |
KLALQLALQALQAALQLA |
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KMTRAQRRAAARRNRWTAR |
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KRPAATKKAGQAKKKKL |
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LGTYTQDFNKFHTFPQTAIGVGAP |
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LIRLWSHLIHIWFQNRRLKWKKK |
|
LKTLATALTKLAKTLTTL |
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LKTLTETLKELTKTLTEL |
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LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTESC |
|
LLIILRARIRKQAHAHSK |
|
LLIILRRPIRKQAHAHSK |
|
LLIILRRRIRKQAHAHSA |
|
LLIILRRRIRKQAHAHSK | |
LNSAGYLLGKINLKALAALAKKIL | |
LNSAGYLLGKLKALAALAKIL |
|
MANLGYWLLALFVTMWTDVGLCKKRPKP |
|
MDAQTRRRERRAEKQAQWKAAN | |
MGLGLHLLVLAAALQGAKKKRKV |
|
MPKKKPTPIQLNP |
|
MVKSKIGSWILVLFVAMWSDVGLCKKRPKP |
|
MVTVLFRRLRIRRACGPPRVRV |
|
NAKTRRHERRRKLAIER | |
PKKKRKV |
|
PKKKRKVALWKTLLKKVLKA |
|
PMLKE |
|
QLALQLALQALQAALQLA |
|
RGGRLSSYSRRRFSTSTGR |
|
RGGRLSYSRRRFSTSTGR |
|
RGGRLSYSRRRFSTSTGRA |
|
RKKRRQRRR | |
RKSSKPIMEKRRRAR |
|
RQARRNRRRALWKTLLKKVLKA |
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RQGAARVTSWLGRQLRIAGKRLEGR |
|
RQGAARVTSWLGRQLRIAGKRLEGRSK |
|
RQIKIWFPNRRMKWKK | |
RQIKIWFQNMRRKWKK |
|
RQIKIWFQNRRMKWKK | |
RQIKIWFQNRRMKWKKLRKKKKKH |
|
RQIRIWFQNRRMRWRR | |
RQPKIWFPNRRMPWKK |
|
RRLSSYSSRRRF |
|
RRMKWKK |
|
RRRRRRRRR | |
RRWRRWWRRWWRRWRR |
|
RVIRVWFQNKRCKDKK | |
RVTSWLGRQLRIAGKRLEGRSK |
|
SWLGRQLRIAGKRLEGRSK |
|
TAKTRYKARRAELIAERR | |
TRQARRNRRRWRERQR |
|
TRRNKRNRIQEQLNRK | |
TRSSRAGLQFPVGRVHRLLRK |
|
TRSSRAGLQWPVGRVHRLLRKGGC |
|
VPALR |
|
VPMLK |
|
VPTLK |
|
VQAILRRNWNQYKIQ |
|
VRLPPPVRLPPPVRLPPP |
|
WFQNRRMKWKK |
|
YGRKKRRQRRR |
|
YGRKKRRQRRRGTSSSSDELSWIIELLEK |
|
YGRKKRRQRRRSVYDFFVWL |
|
The set of 111 know CPPs was balanced with a set of 111 peptides constructed using a 0th order Markov chain derived from the IPI chicken proteome (ipi.CHICK.v3.56
The set of 111 know CPPs was balanced with randomly selected biological peptides. A set of 411 chicken peptides from NCBI with lengths in the range 12–26 was downloaded. Subsets of 111 peptides were selected randomly without replacement to provide multiple balanced datasets. This dataset provides a set of positive examples of known CPPs and assumed negative examples of biological peptides of the same relative molecular size. We assume that most naturally peptides are not cell penetrating.
A set of 34 known non-penetrating cell penetrating peptide analogs and peptide hormones previously used as negative examples was constructed from a search of the literature and are listed in
Non-cell penetrating peptide | Reference |
AGCKNFFWKTFTSC |
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AHALCLTERQIKSNRRMKWKKEN |
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CYFQNCPRG |
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DFDMLRCMLGRVYRPCWQV |
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EILLPNNYNAYESYKYPGMFIALSK |
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FITKALGISYGRKKRRQC |
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FVPIFTHSELQKIREKERNKGQ |
|
GRKKRRQPPQC |
|
GWTLNSAGYLLGKFLPLILRKIVTAL | |
GWTLNSAGYLLGKINLKAPAALAKKIL | |
GWTLNSAGYLLGPHAI |
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GWTNLSAGYLLGPPPGFSPFR |
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HDEFERHAEGTFTSDVSSYLEGQAAKEFIAWLVKGR |
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IAARIKLRSRQHIKLRHL |
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ILRRRIRKQAHAHSK |
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KIWFQNRRMK |
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KKKQYTSIHHGVVEVD |
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KKLSECLKRIGDELDS |
|
KLALKALKAALKLA | |
KLALKLALKALKAA |
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LLGKINLKALAALAKKIL |
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LLKTTALLKTTALLKTTA | |
LLKTTELLKTTELLKTTE | |
LNSAGYLLGKALAALAKKIL | |
LNSAGYLLGKLKALAALAK | |
LRKKKKKH |
|
PVVHLTLRQAGDDFSR |
|
QNLGNQWAVGHLM |
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RPPGFSPFR |
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RQIKIFFQNRRMKFKK | |
RQIKIWFQNRRM |
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RQIKIWFQNRRMKWK |
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TERQIKIWFQNRRMK |
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WSYGLRPG |
|
In order to produce a balanced dataset of both known non-penetrating peptides and known CPPs a set consisting of all 111 known cell penetrating peptides and 111 known non-penetrating cell penetrating analogs was constructed by selecting with replacement from the set of 34 known non-penetrating analogs .
Subsets of the known CPPs of size 34 were selected with replacement and combined with the 34 known non-penetrating cell penetrating analogs to create ten balanced subsets.
For each dataset, we generate a set of basic biochemical properties of each peptide (e.g. mass, size, charge, secondary structure, etc) and other features previously shown to be useful in the prediction of CPPs (e.g. steric bulk and net donated hydrogen bonds)
Feature | Reference |
Length of peptide |
|
Net charge of peptide |
|
Positive charge |
|
Negative charge |
|
Isoelectric point (pI) |
|
Molecular weight |
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Hydropathicity |
|
Number of Each Amino Acid (20 features) |
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Percent composition of each amino acid (20 features) |
|
Percent polar amino acids |
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Percent positive amino acids |
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Percent negative amino acids |
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Percent hydrophobic amino acids |
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Hydrophobicity |
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Lipophilicity |
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Amphiphilicity |
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Water-Octanol Partition Coefficient |
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Steric Bulk |
|
Side chain bulk |
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Net donated hydrogen bonds |
|
Percent α helix |
|
Percent random coil |
|
Percent β sheet |
|
The WEKA Machine Learning Toolkit Version 3.6.1, a freely available software package containing a number of machine learning algorithms for data mining, was used for feature selection, classifier construction, and classifier evaluation
We conducted feature selection to reduce the dimensionality of the feature vectors. Empirical evaluation of a number of different feature selection methods was conducted and the best performance was obtained using a wrapper-based method. The wrapper-based method uses a parallel scatter search algorithm
Our classifier is a support vector machine (SVM) trained via a sequential minimal optimization (SMO) algorithm used in conjunction with the Pearson VII universal kernel
A 0th order Markov chain based on the amino acid frequency of the IPI Chicken Proteome (ipi.CHICK.v3.56)
Name | Role | Sequence (N to C) |
HIV-TAT |
Control(+) | YGRKKRRQRRR-NH2 |
Antennapedia |
Control(+) | RQIKIWFQNRRMKWKK-NH2 |
Pep-1 |
Control(+) | KETWWETWWTEWSQPKKKRKV-NH2 |
negative-1 | Control(-) | TCSSNCQTCPCSSNNCQ-NH2 |
negative-2 |
Control(-) | GLALLGIAVAILVVL-NH2 |
negative-3 | Control(-) | PGNIQMMSVVSMSMTITN-NH2 |
peptide-1 | Predicted CPP | FKIYDKKVRTRVVKH-NH2 |
peptide-2 | Predicted CPP | RASKRDGSWVKKLHRILE-NH2 |
peptide-3 | Predicted CPP | KGTYKKKLMRIPLKGT-NH2 |
peptide-4 | Predicted CPP | LYKKGPAKKGRPPLRGWFH-NH2 |
peptide-5 | Predicted Non-CPP | FFSLPPVTQDWNSD-NH2 |
peptide-6 | Predicted Non-CPP | HSPIIPLGTRFVCHGVT-NH2 |
TP13 |
Known Non-CPP-CPP Analog | LNSAGYLLGKALAALAKKIL-NH2 |
*negative-2 was unable to be synthesized to desired purity levels due to insolubility issues.
Two avian cell lines, Quail SOgE muscle cells
Approximately 100,000 cells per well (both CEFs and SOgEs) were plated onto 12-well tissue culture plates approximately 2 days prior to the experiment and allowed to reach confluency. The cells were changed to serum free media and incubated for 60 minutes prior to experimentation. The cells were then washed with two 1 mL washes of PBS, after which they were exposed to 300 µL of 10 µM peptide in serum free media for 30 minutes, with three replicates per peptide per cell line. The cells were then washed with two 1 mL washes of PBS, and lightly trypsinated to remove any external peptides that may have been attached to the cellular membrane and facilitate the detachment of cells from the tissue culture flask. Centrifugation of the cells was performed at 250 x G for 4 min, and the supernatant aspirated off. Cells were then lysed with 250 µL of 0.1% Triton-X in PBS at 4° C for 10 minutes. A 100 µL aliquot of the cell lysate and a 100 µL aliquot of the 10 µM peptide in serum free media were pipetted onto a 96-well plate. Fluorescence was measured on a Dynex Fluorolite 1000 plate reader at 485/530 nm. The samples were compared to the fluorescence of the added amount of peptide and
The SOgE cells were seeded onto glass tissue microscopy slides (approximately 50,000 cells/well), and allowed two days to reach confluency. The cells were changed to serum free media and incubated for 60 minutes prior to experimentation. The cells were then washed with two 1 mL washes of PBS, after which they were exposed to 300 µL of 10 µM peptide in serum free media for 30 minutes. The cells were then washed with two 1 mL washes of PBS, and then fixed using UltraCruz™Mounting Medium (Santa Cruz Biotechnology) containing a DAPI nuclear stain. The fluorescence was examined using a Nikon Eclipse TE2000-U Inverted Research Microscope with the MetaMorph microscopy imaging software.
The authors acknowledge Dusan Kunec who provided the cell lines and media formulations used for the tissue culture study, and Scott Willard and Jean-Magloire Nguekam Feugang who provided access to and training on the imaging equipment used for the microscopy analysis.