The genetic code is degenerate, and most amino acids are encoded by two to six synonymous codons. Codon usage bias, the preference for certain synonymous codons, is a universal feature of all genomes examined. Synonymous codon mutations were previously thought to be silent; however, a growing body evidence now shows that codon usage regulates protein structure and gene expression through effects on co-translational protein folding, translation efficiency and accuracy, mRNA stability, and transcription. Codon usage regulates the speed of translation elongation, resulting in non-uniform ribosome decoding rates on mRNAs during translation that is adapted to co-translational protein folding process. Biochemical and genetic evidence demonstrate that codon usage plays an important role in regulating protein folding and function in both prokaryotic and eukaryotic organisms. Certain protein structural types are more sensitive than others to the effects of codon usage on protein folding, and predicted intrinsically disordered domains are more prone to misfolding caused by codon usage changes than other domain types. Bioinformatic analyses revealed that gene codon usage correlates with different protein structures in diverse organisms, indicating the existence of a codon usage code for co-translational protein folding. This review focuses on recent literature on the role and mechanism of codon usage in regulating translation kinetics and co-translational protein folding. 5xoCminnRXUHneYwYekFPK Video abstract Video abstract

Messenger RNA (mRNA) secondary structure decreases the elongation rate, as ribosomes must unwind every structure they encounter during translation. Therefore, the strength of mRNA secondary structure is assumed to be reduced in highly translated mRNAs. However, previous studies in vitro reported a positive correlation between mRNA folding strength and protein abundance. The counterintuitive finding suggests that mRNA secondary structure affects translation efficiency in an undetermined manner. Here, we analyzed the folding behavior of mRNA during translation and its effect on translation efficiency. We simulated translation process based on a novel computational model, taking into account the interactions among ribosomes, codon usage and mRNA secondary structures. We showed that mRNA secondary structure shortens ribosomal distance through the dynamics of folding strength. Notably, when adjacent ribosomes are close, mRNA secondary structures between them disappear, and codon usage determines the elongation rate. More importantly, our results showed that the combined effect of mRNA secondary structure and codon usage in highly translated mRNAs causes a short ribosomal distance in structural regions, which in turn eliminates the structures during translation, leading to a high elongation rate. Together, these findings reveal how the dynamics of mRNA secondary structure coupling with codon usage affect translation efficiency.

As a common phenomenon existing in almost all genomes, codon usage bias has been studied for a long time. Codon optimization is a frequently used strategy to accelerate protein synthesis rate. Besides regulating protein translation speed, codon usage bias has also be reported to affect co-translation folding and transcription. P. pastoris is a well-developed expression system, whose efficiency is tightly correlated with commercial value. However, few studies focus on the role of codon usage bias in affecting protein expression in P. pastoris. Besides, many genes in P. pastoris genome show significant negative correlation between codon usage bias and protein structural disorder tendency. It's not known whether this feature is important for their expression. In order to answer these questions, we picked 4P. pastoris gene candidates with strong negative correlation between codon usage bias and protein structural disorder. We then performed full-length codon optimization which completely eliminated the correlation. Protein and RNA assays were then used to compare protein and mRNA levels before and after codon optimization. As a result, codon optimization failed to elevate their protein expression levels, and even resulted in a decrease. As represented by the trypsin sensitivity assays, codon optimization also altered the protein structure of 0616 and 0788. Besides protein, codon optimization also affected mRNA levels. Shown by in vitro and in vivo RNA degradation assays, the mRNA stability of 0616, 0788 and 0135 were also altered by codon optimization. For each gene, the detailed effect may be related with its specific sequence and protein structure. Our results suggest that codon usage bias is an important factor to regulate gene expression level, as well as mRNA and protein stabilities in P. pastoris. "Extreme" codon optimization in genes with strong negative correlation between codon usage bias and protein structural disorder tendency may not be favored. Compromised strategies should be tried if expression is not successful. Besides, codon optimization may affect protein structural conformation more severely in structural disordered proteins.

Codon optimization enhances heterologous gene expression by modulating synonymous codon usage, a critical task in genetic engineering and synthetic biology. Achieving optimal expression requires balancing multiple interdependent factors, such as host codon bias, GC content and mRNA secondary structure, turning optimization into a challenging multiobjective problem. Here, we introduce DeepCodon, a novel deep learning tool focused on preserving functionally important rare codon clusters, which are often overlooked in previous methods. Using Escherichia coli as the host species for gene expression, a protein-CDS translation model was first trained on 1.5 million natural Enterobacteriaceae sequences and then fine-tuned with highly expressed genes. To protect functionally important rare codon clusters, we integrated a conditional probability strategy that preserves conserved rare codons. Compared with conventional approaches, DeepCodon generates sequences that better match host preferences, achieves superior in silico metrics and maintains critical rare codons. Experimental validation of seven low-yield P450s and thirteen AI-designed G3PDHs in E. coli revealed that DeepCodon outperformed traditional methods in nine cases. These results demonstrate DeepCodon's potential as a practical solution for codon optimization.

Expression of tetanus toxin fragment C in E. coli: high level expression by removing rare codons. Tetanus toxin fragment C had been previously expressed in Escherichia coli at 3-4% cell protein. The codon bias for tetanus toxin in Clostridium tetani is very different from that of highly expressed homologous genes in E. coli, resulting in the presence of many rare E. coli codons in the sequence encoding fragment C. We have replaced the coding sequence by sequence optimized for codon usage in E. coli, and show that the expression of fragment C is increased. Although the level of mRNA also increased this appeared to be a secondary consequence of more efficient translation. Complete sequence replacement increased expression to approximately 11-14% cell protein but only after the promoter strength had been improved. Published in Nucleic acids research (1989)

Abstract Dynamic control of gene expression is crucial for cellular adaptation to environmental challenges. mRNA secondary structure is known to be associated with mRNA and protein abundance, but little is known about how mRNA secondary structure affects gene expression dynamics. We report a genome-wide computational analysis of mRNA secondary structure, codon usage, and gene expression in budding yeast. We show that mRNA secondary structure combined with codon optimality regulates gene expression in multiple ways, from transcription to mRNA stability to translation. Moreover, we find that the effect of mRNA secondary structure on mRNA abundance is primarily mediated by transcription, not mRNA stability. Notably, genes with low mRNA secondary structure were substantially enriched for functions relevant to stress response, acting in the mitochondrion, endoplasmic reticulum, and ribosome. On the other hand, genes with high mRNA secondary structure were enriched for functions relevant to cellular maintenance, including macromolecular metabolism and biosynthesis. Our results suggest that mRNA secondary structure affects gene expression through coordination of multiple stages in protein biogenesis, with important consequences for stress response. The coupling of transcription to mRNA stability to translation makes concerted changes in mRNA and protein abundance possible and may amplify the effect of regulation to make quick responses to environmental variations.

Codon usage and secondary structure of mRNA. The specific codon usage pattern of the repetitive unit nucleotide sequence of silk fibroin mRNA suggests that selection has operated on the codon usage to optimize the secondary structure characteristic of the mRNA. The correlation between the stability map of local secondary structure of type I collagen mRNA and the codon usage pattern and the translation rate of the collagen is also implied. Published in Nucleic acids symposium series (1990)

# Which is more important for protein expression mRNA structure or codon optimization? Tags: gene-expression - Score: 13 - Views: 1112 - Answers: 2 - Answered: yes - Asked by: bobthejoe (7877 rep) - Asked: 2012-02-22 - Edited: 2012-03-12 - Site: biology ## Question The field seems extremely divided on the debate. On one hand, artificial experiments have suggested that synonymous mutations don't correlate with gene expression but rather, the mRNA 5' structure is the most important 1. On the other hand, genome wide analysis suggests that tRNA biases are better associated with high expression 2. What other works balance out this discussion? Coding-sequence determinants of gene expression in Escherichia coli Translation efficiency is determined by both codon bias and folding energy An evolutionarily conserved mechanism for controlling the efficiency of protein translation ## Answers ### Answer by Gergana Vandova (score: 11 [ACCEPTED]) This is an excellent question! To my knowledge, there hasn't been a definite answer yet. Recently, I did tons of research on which factors influence protein expression and you should definitely check out the following questions which I asked: Wh

Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli | Nature Biotechnology - Resource - Published: 24 September 2018 # Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli - Guillaume Cambray ORCID: orcid.org/0000-0003-0087-2469 1, 2, - Joao C Guimaraes ORCID: orcid.org/0000-0002-1664-472X 1, 3 & - Adam Paul Arkin ORCID: orcid.org/0000-0002-4999-2931 3, 4 Nature Biotechnology volume 36, pages 1005–1015 (2018) Cite this article ## Abstract Comparative analyses of natural and mutated sequences have been used to probe mechanisms of gene expression, but small sample sizes may produce biased outcomes. We applied an unbiased design-of-experiments approach to disentangle factors suspected to affect translation efficiency in E. coli. We precisely designed 244,000 DNA sequences implementing 56 replicates of a full factorial design to evaluate nucleotide, secondary structure, codon and amino acid properties in combination. For each sequence, we measured reporter transcript abundance and decay, polysome profiles, protein production and growth rates. Associations bet

Analysis of 11,430 recombinant protein production experiments reveals that protein yield is tunable by synonymous codon changes of translation initiation sites | PLOS Computational Biology ## Figures ## Abstract Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann’s ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics ## Abstract Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enha