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A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions.
Chu, Yanyi; Yu, Dan; Li, Yupeng; Huang, Kaixuan; Shen, Yue; Cong, Le; Zhang, Jason; Wang, Mengdi.
Afiliación
  • Chu Y; Center for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA.
  • Yu D; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Li Y; RVAC Medicines, Waltham, MA 02451, USA.
  • Huang K; RVAC Medicines, Waltham, MA 02451, USA.
  • Shen Y; Center for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA.
  • Cong L; RVAC Medicines, Waltham, MA 02451, USA.
  • Zhang J; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Wang M; Zipcode Bio, Weston, MA 02493, USA.
Nat Mach Intell ; 6(4): 449-460, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38855263
ABSTRACT
The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5' UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best known benchmark by up to 5% for predicting the Mean Ribosome Loading, and by up to 8% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5' UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5' UTR optimized for therapeutics.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Mach Intell Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Mach Intell Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos
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