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Table 2 Summary of literature references comparing protein expression levels for native and optimized DNAs in bacterial systems

From: Assessing optimal: inequalities in codon optimization algorithms

First author Optimization algorithma (source) Target(s) Number of constructs Conclusions
Burgess-Brown [40] Proprietary (Genscript, Sigma, and MediGene) Various 30 • 26% of targets show higher expression of soluble protein for optimized over native CDS in E. coli
Kudla [27] CAI GFP 154 • Fluorescence levels span > 1000-fold across different CDSs
• No correlation between fluorescence levels and CAI
• Modest relationship between mRNA 2° structure and GFP fluorescence
Welch [28] PLSR (DNA 2.0) φ29 DNA polymerase 21 • > 100-fold difference in protein yield observed by differently optimized DNAs
Maertens [41] CAI (GeneArt) Various 100 • 24% targets showed ≥ 2× yield for optimized CDS
• 20% targets showed lower expression for optimized CDS
Spencer [42] Undefined Firefly Luciferase 7 • Optimization increased translation speeds ~ 2× with proportional decrease in functional protein
• 2–2.5× yield and solubility increase when recoded for frequent codons in Drosophila melanogaster
Trösemeier [43] CAI (GeneArt) COSEM ova
manA
5
11
• COSEM optimized sequences expressed ≥ 2× the native sequence
• “Ramp” inclusion was necessary for significant boost in protein expression
Konczal [44] CAI (GeneWiz) KRas4B
RalA
Rac1
11
11
11
• “Deoptimization” with ≤ 4 rare codons improves solubility ≥ 4× compared to native CDS
  1. CAI Codon Adaptation Index, PLS partial least squares regression, COSEM Codon-Specific Elongation Model
  2. aThe Kazusa database is reportedly used for codon frequency values by most commercial companies. The COSEM algorithm uses codon frequencies defined by Dong et al. for E. coli with a doubling time of 2.5 h−1