Este artigo é um Preprint
Preprints são relatos preliminares de pesquisa que não foram certificados pela revisão por pares. Eles não devem ser considerados para orientar a prática clínica ou comportamentos relacionados à saúde e não devem ser publicados na mídia como informação estabelecida.
Preprints publicados online permitem que os autores recebam feedback rápido, e toda a comunidade científica pode avaliar o trabalho independentemente e responder adequadamente. Estes comentários são publicados juntamente com os preprints para qualquer pessoa ler e servir como uma avaliação pós-publicação.
DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design
Preprint
em Inglês
| bioRxiv
| ID: ppbiorxiv-518473
ABSTRACT
Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities. Author summaryCompared with small-molecule drugs, aptamer drugs are short RNAs/DNAs that can specifically bind to targets with high strength. With the interest of discovering novel aptamer drugs as an alternative to address the long-lasting COVID-19 pandemic, in this research, we developed an artificial intelligence (AI) framework for the in silico design of novel aptamer drugs that can prevent the SARS-CoV-2 virus from entering human cells. Our research is valuable as we explore a novel approach for the treatment of SARS-CoV-2 infection and the AI framework could be applied to address future health crises.
cc_by
Texto completo:
Disponível
Coleções:
Preprints
Base de dados:
bioRxiv
Tipo de estudo:
Revisão sistemática
Idioma:
Inglês
Ano de publicação:
2022
Tipo de documento:
Preprint