Constrained codon optimization by dynamic programming

Pham, Tuan D.; O'Connell, J.; Crane, Daniel

doi:10.1109/isimp.2004.1434023

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Currently, various optimization algorithms are utilized, such as the genetic algorithm ( Błażej et al, 2018 ), multi-objective artificial bee colony ( Gonzalez-Sanchez et al, 2019 ), Ribotree Monte Carlo ( Leppek et al, 2022 ), and dynamic programming ( Pham et al, 2004 ; Taneda and Asai, 2020 ), to identify codon combinations with desired characteristics. In several studies, the use of recurrent neural networks for codon optimization in heterologous protein expression has been presented in Chinese hamster ( Gricetulus griseus ) ovary cells ( Goulet et al, 2023 ) and E. coli ( Jain et al, 2023 ).…”

Section: The Quantitative Assessment Of Codon Usage and Optimizationmentioning

confidence: 99%

Codon-optimization in gene therapy: promises, prospects and challenges

Paremskaia,

Kogan,

Murashkina

et al. 2024

Front. Bioeng. Biotechnol.

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Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code’s redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.

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