Integrated mRNA sequence optimization using deep learning.
Brief Bioinform
; 24(1)2023 01 19.
Artigo
em Inglês
| MEDLINE | ID: covidwho-2230450
ABSTRACT
The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps:
open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics.Palavras-chave
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
COVID-19
Tipo de estudo:
Estudo prognóstico
/
Ensaios controlados aleatorizados
Tópicos:
Vacinas
Limite:
Humanos
Idioma:
Inglês
Assunto da revista:
Biologia
/
Informática Médica
Ano de publicação:
2023
Tipo de documento:
Artigo
País de afiliação:
Bib
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