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Highly efficient delivery of functional cargoes by the synergistic effect of GAG binding motifs and cell-penetrating peptides.

ABSTRACT: Protein transduction domains (PTDs) are powerful nongenetic tools that allow intracellular delivery of conjugated cargoes to modify cell behavior. Their use in biomedicine has been hampered by inefficient delivery to nuclear and cytoplasmic targets. Here we overcame this deficiency by developing a series of novel fusion proteins that couple a membrane-docking peptide to heparan sulfate glycosaminoglycans (GAGs) with a PTD. We showed that this GET (GAG-binding enhanced transduction) system could deliver enzymes (Cre, neomycin phosphotransferase), transcription factors (NANOG, MYOD), antibodies, native proteins (cytochrome C), magnetic nanoparticles (MNPs), and nucleic acids [plasmid (p)DNA, modified (mod)RNA, and small inhibitory RNA] at efficiencies of up to two orders of magnitude higher than previously reported in cell types considered hard to transduce, such as mouse embryonic stem cells (mESCs), human ESCs (hESCs), and induced pluripotent stem cells (hiPSCs). This technology represents an efficient strategy for controlling cell labeling and directing cell fate or behavior that has broad applicability for basic research, disease modeling, and clinical application.

SUBMITTER: Dixon JE

PROVIDER: S-EPMC4725514 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Highly efficient delivery of functional cargoes by the synergistic effect of GAG binding motifs and cell-penetrating peptides.

Dixon James E JE Osman Gizem G Morris Gavin E GE Markides Hareklea H Rotherham Michael M Bayoussef Zahia Z El Haj Alicia J AJ Denning Chris C Shakesheff Kevin M KM

Proceedings of the National Academy of Sciences of the United States of America 20160105 3

Protein transduction domains (PTDs) are powerful nongenetic tools that allow intracellular delivery of conjugated cargoes to modify cell behavior. Their use in biomedicine has been hampered by inefficient delivery to nuclear and cytoplasmic targets. Here we overcame this deficiency by developing a series of novel fusion proteins that couple a membrane-docking peptide to heparan sulfate glycosaminoglycans (GAGs) with a PTD. We showed that this GET (GAG-binding enhanced transduction) system could ...[more]

PMID: 26733682

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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/.

Dataset Information

Highly efficient delivery of functional cargoes by the synergistic effect of GAG binding motifs and cell-penetrating peptides.

Publications

Highly efficient delivery of functional cargoes by the synergistic effect of GAG binding motifs and cell-penetrating peptides.

Similar Datasets

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