In summary, iCodon chooses ideal codons for incorporation into designed coding sequences to reach desired gene expression levels. Therefore, iCodon can also be used to visualize the predicted mRNA stability based on the codon composition of any coding sequence. iCodon uses a predictive model of mRNA stability ( Medina-Muñoz et al., 2021) as a guide for supervising the design of sequences. Here, we developed a tool named iCodon ( that optimizes coding regions with synonymous codon substitutions to increase mRNA stability and therefore protein expression (e.g., to design highly expressed reporters), or deoptimize sequences with synonymous codon substitutions to decrease mRNA stability (e.g., to design a sequence with decreased expression). Therefore, a method for codon optimization using codon optimality represents a novel approach for in silico gene design. For example, the codon usage bias for some amino acids (e.g., Arginine and Threonine) differs drastically from codon optimality ( Q. Yet, in vertebrates, weak positive correlations have been observed between codon usage bias and codon optimality ( Bazzini et al., 2016 Q. Therefore, we hypothesized that the model could be used as a tool for the design of synonymous coding sequences with differing stability characteristics depending on the desired application.Įxisting methods to perform codon optimization are mainly based on codon usage bias ( Burgess-Brown et al., 2008 Fuglsang, 2003 Puigbo, Guzman, Romeu, & Garcia-Vallve, 2007 G.
Wu et al., 2019) and mouse cells ( Herzog et al., 2017), as well as Xenopus and zebrafish embryos ( Bazzini et al., 2016 Medina-Muñoz et al., 2021), this model has revealed that codon composition is a major determinant of mRNA stability during early embryogenesis and dictates mRNA levels in conjunction with other cis-regulatory elements (e.g., microRNA and m 6A) in human and mouse cells as well as in zebrafish and Xenopus embryos ( Medina-Muñoz et al., 2021). Trained with multiple profiles of mRNA stability for thousands of genes obtained from human ( Q. Specifically, to determine the regulatory strength of codon optimality in vertebrates, we have recently developed a machine learning model that predicts mRNA stability based on codon composition ( Medina-Muñoz et al., 2021). Codon optimality is the most pervasive mechanism underlying mRNA stability in yeast ( Cheng, Maier, Avsec, Rus, & Gagneur, 2017) and vertebrates ( Medina-Muñoz et al., 2021). Wu et al., 2019) as well as in other species ( Boël et al., 2016 Burow et al., 2018 de Freitas Nascimento et al., 2018 Harigaya & Parker, 2016 Jeacock, Faria, & Horn, 2018 Presnyak et al., 2015 Radhakrishnan et al., 2016), a process referred as codon optimality ( Presnyak et al., 2015). Recent studies have revealed that translation strongly affects mRNA stability in cis in a codon-dependent manner in vertebrates ( Bazzini et al., 2016 Mishima & Tomari, 2016 Q. Moreover, synonymous codon substitutions can dramatically affect messenger RNA (mRNA) stability and therefore protein production ( Boël et al., 2016 de Freitas Nascimento, Kelly, Sunter, & Carrington, 2018 Gouy & Gautier, 1982 Q. Synonymous codons are used with different frequencies in the coding genome, a phenomenon known as codon usage bias ( Sharp & Li, 1987). Long regarded as interchangeable, these codons are not equivalent from a regulatory point of view ( Gouy & Gautier, 1982).
The codons encoding for the same amino acid are called synonymous or silent codons.
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The genetic code is degenerate, as most amino acids are encoded by multiple codons ( Gouy & Gautier, 1982).