ArticleScientific reports2020
Codon optimization with deep learning to enhance protein expression.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.
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107 citing papers in PubMed, 198 citations in OpenAlex.
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- Review
- Deep Learning-Guided Reverse Translation Enhances Soluble Expression of Recombinant Proteins inInternational journal of molecular sciences · 2026Article
- pCODE tRNA Expression Plasmids Compensate for Rare Codons in Recombinant Protein Production.ACS synthetic biology · 2026Article
- Molecular engineering of Komagataella phaffii for venom toxin production.Applied microbiology and biotechnology · 2026Review
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- A critical signaling role for diacylglycerol in phagocytosis ofbioRxiv : the preprint server for biology · 2026Article
- Cross-species gene redesign leveraging ortholog information and generative modeling.Nature communications · 2026Article
- In Silico Design and Characterization of a Multi-Epitope Vaccine Candidate AgainstMicroorganisms · 2026Article
- An Alignment Free Framework for Taxonomic Inference From Codon and Codon-Pair Usage.Ecology and evolution · 2026Article
- Pichia-CLM: A language model-based codon optimization pipeline forProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Design and computational evaluation of a prophylactic and therapeutic multi-epitope vaccine candidate against cervical cancer.Virology journal · 2026Article
47 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning. First, we introduce the concept of codon boxes, via which DNA sequences can be recoded into codon box sequences while ignoring the order of bases. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. The codon optimization models for Escherichia Coli were trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. In addition to the comparison of the codon adaptation index, protein expression experiments for plasmodium falciparum candidate vaccine and polymerase acidic protein were implemented for comparison with the original sequences and the optimized sequences from Genewiz and ThermoFisher. The results show that our method for enhancing protein expression is efficient and competitive.
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