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Discovery and Mechanism of Highly Efficient Cyclic Cell-Penetrating Peptides.

ABSTRACT: Previous cell-penetrating peptides (CPPs) generally have low cytosolic delivery efficiencies, because of inefficient endosomal escape. In this study, a family of small, amphipathic cyclic peptides was found to be highly efficient CPPs, with cytosolic delivery efficiencies of up to 120% (compared to 2.0% for Tat). These cyclic CPPs bind directly to the plasma membrane phospholipids and enter mammalian cells via endocytosis, followed by efficient release from the endosome. Their total cellular uptake efficiency correlates positively with the binding affinity for the plasma membrane, whereas their endosomal escape efficiency increases with the endosomal membrane-binding affinity. The cyclic CPPs induce membrane curvature on giant unilamellar vesicles and budding of small vesicles, which subsequently collapse into amorphous lipid/peptide aggregates. These data suggest that cyclic CPPs exit the endosome by binding to the endosomal membrane and inducing CPP-enriched lipid domains to bud off as small vesicles. Together with their high proteolytic stability, low cytotoxicity, and oral bioavailability, these cyclic CPPs should provide a powerful system for intracellular delivery of therapeutic agents and chemical probes.

SUBMITTER: Qian Z

PROVIDER: S-EPMC8562596 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCell penetrating peptides have gained much recognition as a versatile transport vehicle for the intracellular delivery of wide range of cargoes (i.e. oligonucelotides, small molecules, proteins, etc.), that otherwise lack bioavailability, thus offering great potential as future therapeutics. Keeping in mind the therapeutic importance of these peptides, we have developed in silico methods for the prediction of cell penetrating peptides, which can be used for rapid screening of such peptides prior to their synthesis.MethodsIn the present study, support vector machine (SVM)-based models have been developed for predicting and designing highly effective cell penetrating peptides. Various features like amino acid composition, dipeptide composition, binary profile of patterns, and physicochemical properties have been used as input features. The main dataset used in this study consists of 708 peptides. In addition, we have identified various motifs in cell penetrating peptides, and used these motifs for developing a hybrid prediction model. Performance of our method was evaluated on an independent dataset and also compared with that of the existing methods.ResultsIn cell penetrating peptides, certain residues (e.g. Arg, Lys, Pro, Trp, Leu, and Ala) are preferred at specific locations. Thus, it was possible to discriminate cell-penetrating peptides from non-cell penetrating peptides based on amino acid composition. All models were evaluated using five-fold cross-validation technique. We have achieved a maximum accuracy of 97.40% using the hybrid model that combines motif information and binary profile of the peptides. On independent dataset, we achieved maximum accuracy of 81.31% with MCC of 0.63.ConclusionThe present study demonstrates that features like amino acid composition, binary profile of patterns and motifs, can be used to train an SVM classifier that can predict cell penetrating peptides with higher accuracy. The hybrid model described in this study achieved more accuracy than the previous methods and thus may complement the existing methods. Based on the above study, a user-friendly web server CellPPD has been developed to help the biologists, where a user can predict and design CPPs with much ease. CellPPD web server is freely accessible at http://crdd.osdd.net/raghava/cellppd/.

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Discovery and Mechanism of Highly Efficient Cyclic Cell-Penetrating Peptides.

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