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Analysis of RNA translation with a deep learning architecture provides new insight into translation control
Open accessPreprint — published as 10.1093/nar/gkaf277
Abstract
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human transcripts, our model learned hidden rules of translation control and achieved a near perfect prediction of canonical translation sites across entire human transcriptome. Surprisingly, this model revealed a new role of codon usage in regulating translation termination, which was experimentally validated. We also identified thousands of new open reading frames in mRNAs or lncRNAs, some of which were confirmed experimentally. The model trained with human mRNAs achieved high prediction accuracy of canonical translation sites in all eukaryotes and good prediction in polycistronic transcripts from prokaryotes or RNA viruses, suggesting a high degree of conservation in translation control. Collectively, we present a general and efficient deep learning model for RNA translation, generating new insights into the complexity of translation regulation.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Regulation of Translation Initiation in Eukaryotes: Mechanisms and Biological Targets
Sonenberg, N., & Hinnebusch, A. G. (2009) · Cell · doi:10.1016/j.cell.2009.01.042 · Cited by 3,304 · cited 1×
“In eukaryotes, translation initiation is generally mediated by the 5 ¢ -cap, which recruits various translation initiation factors and small ribosomal subunit to form preinitiation complex (16).”
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Metadata
- Authors
- Fan, X., Chang, T., Chen, C., Hafner, M., & Wang, Z.
- Affiliations
- National Institutes of Health (US)
- Shanghai Institute of Nutrition and Health (CN)
- Southern University of Science and Technology (CN)
- Corresponding author
- Fan, Xiaojuan
- Wang, Zefeng
- Venue
- Nucleic Acids Research
- Publisher
- openRxiv
- Published
- 2023-07-08
- Volume
- 53
- Issue
- 7
- ISSN
- 0305-1048 (ISSN-L)
- PMC
- PMC11992669
- OpenAlex
- W4409387374
- Cited by
- 12
- References
- 2
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 1 · 2023: 1
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (32030064, 31730110, 32250013, 32100430, 91940303)
- Chinese Academy of Sciences (XDB38040100 to Z.W., XDB38040100)
- Zhejiang University (SN-ZJU-SIAS009)
- National Institutes of Health
- National Key Research and Development Program of China (2021YFA1300503, 2018YFA0107602)
- National Cancer Institute (R01CA220314)
- National Institute on Minority Health and Health Disparities
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- RNA translation, translation initiation sites, codon usage, translational regulation, open reading frames, long non-coding RNA, polycistronic transcripts, deep neural network, human transcriptome
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