Enhancing mRNA translation efficiency with discriminative and generative artificial intelligence by optimizing 5' UTR sequences.

Yu Liu, Chunmei Cui, Limei Liu, Qinghua Cui

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Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Yu LiuDepartment of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China.

Chunmei CuiDepartment of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China.

Limei LiuDepartment of Physiology and Pathophysiology, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China.

Qinghua CuiDepartment of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mRNA-based therapeutics, notably mRNA vaccines, represent a new era of powerful tools to combat various diseases. However, the relatively low translation efficiency of exogenous mRNA often limits its wide application. Here, we propose a computational framework called UTailoR (UTR tailor), which significantly improves the challenge by optimizing 5' UTR sequences based on a two-step artificial intelligence strategy. We first develop a deep-learning-based discriminative model for predicting mRNA translation efficiency with 5' UTR sequences and then present a generative model to generate optimized 5' UTR sequences, which are designed to be highly close to the original sequences but predicted to result in high translation efficiency. The experimental results show that the UTailoR-optimized sequences outstrip the corresponding original sequences by ∼200%. This work provides an efficient and convenient method for mRNA 5' UTR optimization, which can be easily accessed online.

Indexed as

artificial intelligenceBiochemistry

Identifiers

PMID41069846

DOI10.1016/j.isci.2025.113544

PMCPMC12506572

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.