Integrated mRNA sequence optimization using deep learning

Author:

Gong Haoran12ORCID,Wen Jianguo34,Luo Ruihan1ORCID,Feng Yuzhou1,Guo JingJing12,Fu Hongguang5,Zhou Xiaobo346

Affiliation:

1. West China Biomedical Big Data Center, West China Hospital, Sichuan University , Chengdu 610041 , China

2. Med-X Center for Informatics, Sichuan University , Chengdu 610041 , China

3. Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, 77030 , USA

4. McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX 77030 , USA

5. University of Electronic Science and Technology of China , Chengdu 611731 , China

6. School of Dentistry, The University of Texas Health Science Center at Houston , Houston, TX 77030 , USA

Abstract

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.

Funder

NSF

NIH

National Natural Science Foundation of China

Sichuan Science and Technology Program

Sichuan University

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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