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In silico screening, analysis, and modelling for a novel anticancer peptide
Cancer is currently one of the leading causes of mortality and morbidity worldwide. Most anticancer therapies rely on small molecule drugs (<0.5 kDa). As with all small molecule drugs, chemotherapy is highly toxic and presents many off-target side effects. Peptide drugs offer improved specificity an...
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| Format: | Thesis |
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AUC Knowledge Fountain
2016
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| Summary: | Cancer is currently one of the leading causes of mortality and morbidity worldwide. Most anticancer therapies rely on small molecule drugs (<0.5 kDa). As with all small molecule drugs, chemotherapy is highly toxic and presents many off-target side effects. Peptide drugs offer improved specificity and are cheaper and more accessible to manufacture. In this study, we have developed a support vector machine (SVM) model in order to detect peptide sequences with potential anticancer activity through scanning the Red Sea Metagenomic library. Furthermore, we conducted an in silico study in order to analyze one of the peptides returned by the SVM pipeline and assessed its cytotoxicity and the mode of cell death by conducting MTT and Annexin V staining assays, respectively. We observed that the selected anticancer peptide contains the C-terminal portion of the homeodomain structure, of human Pax6, an antennapedia homeodomain region, and can bind DNA. Furthermore, we observed dose-response cytotoxicity of HepG2 cells with our peptide. No such cytotoxicity was observed in HeLa cells; a morphological change, however, was observed. We examined the cytotoxicity of our drug against 1BR-hTERT normal skin cells. Our peptide drug induced dose-dependent cytotoxicity that was markedly weaker than that of cancer treated cells. Together our data illustrates the isolation of one peptide drug candidate from the AUC Red Sea metagenomic library; furthermore, we were able to observe the selective dose-dependent reduction of HepG2 cell viability |
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