Machine learning-based analysis of the impact of 5′ untranslated region on protein expression

The 5' untranslated region (5'UTR) plays a crucial regulatory role in messenger RNA (mRNA), with modified 5'UTRs extensively utilized in vaccine production, gene therapy, etc. Nevertheless, manually optimizing 5'UTRs may encounter difficulties in balancing the effects of various cis-elements. Consequently, multiple 5'UTR libraries have been created, and machine learning models have been employed to analyze and predict translation efficiency (TE) and protein expression, providing insights into critical regulatory features. On the one hand, these screening libraries, based on TE and mean ribosome load, struggle to accurately quantify protein expression; on the other hand, a precise method for quantifying 5'UTRs necessitates a significantly costlier library. To resolve this dilemma, we constructed a library utilizing firefly luciferase as the reporter to measure accurate protein expression. In addition, we optimized the library construction method by clustering mRNA sequences to reduce redundant data and minimize the size of the dataset. This dual strategy by increasing accuracy and reducing dataset size was found to be effective in predicting the 5'UTRs from the PC3 cell line.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2025-08-16
DOI
https://doi.org/10.1093/nar/gkaf861
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine learning-based analysis of the impact of 5′ untranslated region on protein expression

Sujia Liu, Yannan Li, Shaoting Li, Jianying Han et al.
Nucleic Acids Research
RNA modifications and cancer
article

Machine learning-based analysis of the impact of 5′ untranslated region on protein expression

Sujia Liu, Yannan Li, Shaoting Li, Jianying Han, Yin Zhu, Shu‐Ping Zhao, Wentao Liao, Bingwei Wang, Haifeng Zhu, Hongmei Zhang, Sen Chen, Linfeng Wang, Shuyu Li, Ji Zeng, Jia-Xin Huang, Jiahao Wu, Jiaqi Lin, Haiyan Zeng
article en

Abstract

The 5' untranslated region (5'UTR) plays a crucial regulatory role in messenger RNA (mRNA), with modified 5'UTRs extensively utilized in vaccine production, gene therapy, etc. Nevertheless, manually optimizing 5'UTRs may encounter difficulties in balancing the effects of various cis-elements. Consequently, multiple 5'UTR libraries have been created, and machine learning models have been employed to analyze and predict translation efficiency (TE) and protein expression, providing insights into critical regulatory features. On the one hand, these screening libraries, based on TE and mean ribosome load, struggle to accurately quantify protein expression; on the other hand, a precise method for quantifying 5'UTRs necessitates a significantly costlier library. To resolve this dilemma, we constructed a library utilizing firefly luciferase as the reporter to measure accurate protein expression. In addition, we optimized the library construction method by clustering mRNA sequences to reduce redundant data and minimize the size of the dataset. This dual strategy by increasing accuracy and reducing dataset size was found to be effective in predicting the 5'UTRs from the PC3 cell line.

Nucleic Acids ResearchVol. 53(17)
Guangdong University of Technology (CN), Sun Yat-sen University (CN), Guangdong Pharmaceutical University (CN), Dalian University of Technology (CN), Dalian University (CN), Sun Yat-sen University Cancer Center (CN), Guangdong Provincial People's Hospital (CN)
Openalex Percentile: Top 79%
RNA modifications and cancer
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.