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| http://dx.doi.org/10.1038/mt.2012.40 | DOI Listing |
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Transduction of jellyfish superoxide dismutase mediated by TAT peptide ameliorates HO-induced oxidative stress in HaCaT cells.
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Superoxide dismutase (SOD) plays important roles in the balance of oxidation and antioxidation in body mostly by scavenging superoxide anion free radicals (O). Previously, we reported a novel Cu/Zn SOD from jellyfish Cyanea capillata, named CcSOD1, which exhibited excellent SOD activity and high stability. TAT peptide is a common type of cell penetrating peptides (CPPs) that efficiently deliver extracellular biomacromolecules into cytoplasm.
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Cell-Penetrating Peptides (CPPs) are a crucial carrier for drug delivery. Since the process of synthesizing new CPPs in the laboratory is both time- and resource-consuming, computational methods to predict potential CPPs can be used to find CPPs to enhance the development of CPPs in therapy. In this study, EnDM-CPP is proposed, which combines machine learning algorithms (SVM and CatBoost) with convolutional neural networks (CNN and TextCNN).
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Peptides have been extensively studied in nanomedicine with great bioactivity and biocompatibility; however, their poor cell-membrane-penetrating properties and nonselectivity greatly limit their clinical applications. In this study, tumor-targeting therapy was achieved by modifying our previously developed efficient peptide vector with the cancer-targeting peptide RGD, enabling it to specifically target tumor cells with a high expression of RGD-binding receptors. B-cell lymphoma-2 antisense oligonucleotides were selected as the target model to validate the effectiveness of the delivery carriers.
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School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, United Kingdom of Great Britain and Northern Ireland. Electronic address:
Cell-penetrating peptides (CPP) have gained rapid attention over the last 25 years; this is attributed to their versatility, customisation, and 'Trojan horse' delivery that evades the immune system. However, the current CPP rational design process is limited, as it requires several rounds of peptide synthesis, prediction and wet-lab validation, which is expensive, time-consuming and requires extensive knowledge in peptide chemistry. Artificial intelligence (AI) has emerged as a promising alternative which can augment the design process, for example by determining physiochemical characteristics, secondary structure, solvent accessibility, disorder and flexibility, as well as predicting in vivo behaviour such as toxicity and peptidase degradation.
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