ArticleBioinformatics (Oxford, England)2024
CodonBERT: a BERT-based architecture tailored for codon optimization using the cross-attention mechanism.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Toward Rational Design of Precision-Fermented Milk Proteins: Integrating Cross-Species Selection, Post-Translational Modification, and AI Optimization.Comprehensive reviews in food science and food safety · 2026Review
- RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design.Genome biology · 2026Article
- Recombinant Protein Drugs: A 2025 Update.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2026Review
- VaxLab: integrated platform for rapid multistrategy mRNA vaccine design.Experimental & molecular medicine · 2026Article
- Cross-species gene redesign leveraging ortholog information and generative modeling.Nature communications · 2026Article
- Advancing codon language modeling with synonymous codon constrained masking.Nucleic acids research · 2026Article
- Accelerate the Highly Efficient Development of mRNA Vaccines Through Advanced Computational Methods.MedComm · 2026Review
- Engineering mammalian protein secretion: Toward the convergence of high-throughput biology and computational methods.Cell systems · 2025Review
- DeepCodon: A deep learning codon-optimization model to enhance protein expression.Biodesign research · 2025Article
- codonGPT: reinforcement learning on a generative language model enables scalable mRNA design.Nucleic acids research · 2025Article
- ARCADE: Controllable Codon Design from Foundation Models via Activation Engineering.bioRxiv : the preprint server for biology · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Predicting gene sequences with AI to study codon usage patterns.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AI in microbiome-related healthcare.Microbial biotechnology · 2024Review
- Annotation Vocabulary (Might Be) All You Need.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
11 authors.
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Abstract
motivationDue to the varying delivery methods of mRNA vaccines, codon optimization plays a critical role in vaccine design to improve the stability and expression of proteins in specific tissues. Considering the many-to-one relationship between synonymous codons and amino acids, the number of mRNA sequences encoding the same amino acid sequence could be enormous. Finding stable and highly expressed mRNA sequences from the vast sequence space using in silico methods can generally be viewed as a path-search problem or a machine translation problem. However, current deep learning-based methods inspired by machine translation may have some limitations, such as recurrent neural networks, which have a weak ability to capture the long-term dependencies of codon preferences.
resultsWe develop a BERT-based architecture that uses the cross-attention mechanism for codon optimization. In CodonBERT, the codon sequence is randomly masked with each codon serving as a key and a value. In the meantime, the amino acid sequence is used as the query. CodonBERT was trained on high-expression transcripts from Human Protein Atlas mixed with different proportions of high codon adaptation index codon sequences. The result showed that CodonBERT can effectively capture the long-term dependencies between codons and amino acids, suggesting that it can be used as a customized training framework for specific optimization targets. AVAILABILITY AND IMPLEMENTATION: CodonBERT is freely available on https://github.com/FPPGroup/CodonBERT.
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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.