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Cell delivery peptides for small interfering RNAs targeting SARS-CoV-2 new variants through a bioinformatics and deep learning design
Ricardo González
Pedro R Figueiredo
Alexandra Teresa Pires Carvalho
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://doi.org/10.1101/2022.02.09.479755
https://www.biorxiv.org/content/10.1101/2022.02.09.479755v1
Nucleic acid technologies with designed delivery systems have surged as one the most promising therapies of the future, due to their contribution in combating SARS-CoV-2 severe disease. Nevertheless, the emergence of new variants of concern still represents a real threat in the years to come. It is here that the use of small interfering RNA sequences to inhibit gene expression and, thus, protein synthesis, may complement the already developed vaccines, with faster design and production. Here, we have designed new sequences targeting COVID-19 variants and other related viral diseases through bioinformatics, while also addressing the limited number of delivery peptides by a deep learning approach. Two sequences databases were produced, from which 62 were able to target the virus mRNA, and ten displayed properties present in delivery peptides, which we compared to the broad use TAT delivery peptide.
medRxiv and bioRxiv
10-02-2022
Preimpreso
https://www.biorxiv.org/
Inglés
Epidemia COVID-19
Público en general
VIRUS RESPIRATORIOS
Versión publicada
publishedVersion - Versión publicada
Aparece en las colecciones: Artículos científicos

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