Abstract
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.
Highlights
Cell penetrating peptides (CPPs), 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 [1]
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 bilayer of the cell, which takes into account the hydrophobic properties of most of these peptides [2]
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
Summary
Cell penetrating peptides (CPPs), 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 [1]. 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 bilayer of the cell, which takes into account the hydrophobic properties of most of these peptides [2]. The current view is that CPP internalization is accomplished predominantly by endocytosis [2]. Both flow cytometry and fluorescence microscopy have been used to study the uptake of CPPs into cells. Care must be used with these methods to avoid artifacts because traditional methodologies for these techniques can incorrectly show a high concentration of CPPs localizing to the cell nucleus or a higher than actual concentration of CPPs being taken into the cell [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
