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An in-silico approach to design potential siRNAs against the ORF57 of Kaposi’s sarcoma-associated herpesvirus
Genomics & Informatics ; : e47-2021.
Artigo em Inglês | WPRIM | ID: wpr-914321
ABSTRACT
Kaposi's sarcoma-associated herpesvirus (KSHV) is one of the few human oncogenic viruses, which causes a variety of malignancies, including Kaposi's sarcoma, multicentric Castleman disease, and primary effusion lymphoma, particularly in human immunodeficiency virus patients. The currently available treatment options cannot always prevent the invasion and dissemination of this virus. In recent times, siRNA-based therapeutics are gaining prominence over conventional medications as siRNA can be designed to target almost any gene of interest. The ORF57 is a crucial regulatory protein for lytic gene expression of KSHV. Disruption of this gene translation will inevitably inhibit the replication of the virus in the host cell. Therefore, the ORF57 of KSHV could be a potential target for designing siRNA-based therapeutics. Considering both sequence preferences and target site accessibility, several online tools (i-SCORE Designer, Sfold web server) had been utilized to predict the siRNA guide strand against the ORF57. Subsequently, off-target filtration (BLAST), conservancy test (fuzznuc), and thermodynamics analysis (RNAcofold, RNAalifold, and RNA Structure web server) were also performed to select the most suitable siRNA sequences. Finally, two siRNAs were identified that passed all of the filtration phases and fulfilled the thermodynamic criteria. We hope that the siRNAs predicted in this study would be helpful for the development of new effective therapeutics against KSHV.
Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Tipo de estudo: Ensaio Clínico Controlado / Estudo prognóstico Idioma: Inglês Revista: Genomics & Informatics Ano de publicação: 2021 Tipo de documento: Artigo

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Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Tipo de estudo: Ensaio Clínico Controlado / Estudo prognóstico Idioma: Inglês Revista: Genomics & Informatics Ano de publicação: 2021 Tipo de documento: Artigo