Improving heterologous membrane protein production in Escherichia coli by combining transcriptional tuning and codon usage algorithms

Abstract

High-level, recombinant production of membrane-integrated proteins in Escherichia coli is extremely relevant for many purposes, but has also been proven challenging. Here we study a combination of transcriptional fine-tuning in E. coli LEMO21(DE3) with different codon usage algorithms for heterologous production of membrane proteins. The overexpression of 6 different membrane proteins is compared for the wild-type gene codon usage variant, a commercially codon-optimized variant, and a codon-harmonized variant. We show that transcriptional fine-tuning plays a major role in improving the production of all tested proteins. Moreover, different codon usage variants significantly improved production of some of the tested proteins. However, not a single algorithm performed consistently best for the membrane-integrated production of the 6 tested proteins. In conclusion, for improving heterologous membrane protein production in E. coli, the major effect is accomplished by transcriptional tuning. In addition, further improvements may be realized by attempting different codon usage variants, such as codon harmonized variants, which can now be easily generated through our online Codon Harmonizer tool.

Improved protein production and codon optimization analyses in Escherichia coli by bicistronic design

“…These variants include a codon‐harmonized (H) variant (Angov et al ., ), a multiparameter codon‐optimized variant generated using GeneArt's GeneOptimizer software (Opt; Raab et al ., ) and a tRNA codon‐optimized (tRNA) variant (Table S1). Codon harmonization copies the codon usage landscape from the original host to the new host (Angov et al ., ; Claassens et al ., ). GeneArt's GeneOptimizer algorithm performs multiparametric optimization with an apparent preference for common codons as it generated a sequence with the highest Codon Adaptation Index (> 0.9, Table S1; Raab et al ., ).…”

“…These variants include a codon-harmonized (H) variant (Angov et al, 2011), a multiparameter codon-optimized variant generated using GeneArt's GeneOptimizer software (Opt; Raab et al, 2010) and a tRNA codon-optimized (tRNA) variant (Table S1). Codon harmonization copies the codon usage landscape from the original host to the new host (Angov et al, 2011;Claassens et al, 2017). GeneArt's GeneOptimizer algorithm performs multiparametric optimization with an apparent preference for common codons as it generated a sequence with the The regularly used GFPuv (GFPuv-RU) sequence is compared to an optimized sequence (GFPuv-Opt), Escherichia coli tRNA-optimized sequence (GFPuv-tRNA), E. coli tRNA-optimized sequence with subsequent minimalized free energy (GFPuv-tRNA-dG), a minimal free energy transcript (GFPuv-dG) and an E. coli harmonized sequence (GFPuv-H).…”

“…Many codon optimization algorithms have been developed aiming to improve heterologous protein production (Gould et al, 2014), although with varying success rates in terms of increased functional protein production (Maertens et al, 2010;Gustafsson et al, 2012;Claassens et al, 2017;Mignon et al, 2018). This variety can be partially explained by the introduction of new secondary structures within the transcript due to synonymous codon changes (Nørholm et al, 2013;Mirzadeh et al, 2015).…”

Smoothing membrane protein structure determination by initial upstream stage improvements

“…Therefore, in this approach, synonymous codons from E. coli were selected that match as closely as possible the codon usage frequency used in the native gene, unless empirical structure calculations show that the codons are associated with putative link/end segments which therefore should be translated slowly (Angov et al 2008). Claassens et al (2017) studied the performance of this codon harmonization algorithm and compared with the wild-type variant and optimized gene variants (resorting to proprietary GeneOptimizer algorithm from GeneArt) using different proton-pumping rhodopsins and enzymes from archaea, bacteria, and eukarya. Codon harmonization was performed using a codon harmonizer tool (http://codonharmonizer.systemsbiology.nl) based on the harmonization algorithm initially proposed by Angov et al (2008), and uses the codon usage frequency tables for the native and expression hosts, based on all codons in the protein-coding genes annotated in NCBI genome assemblies as inputs.…”

“…In general, harmonization is beneficial for increasing membrane-embedded production compared to wildtype variants for some proteins, for which in this study, the wild-type CHI score is also highest (as in the case of leptosphaeria rhodopsin, CHI = 0.279). Moreover, when the codon landscape of the wild-type gene in E. coli largely deviates from the landscape in the native hosts, harmonization seems to be a promising approach for increasing MP production (Claassens et al 2017). Recent developments point out that irrespective the algorithm used, using a bicistronic design (in comparison with a monocistronic design) does improve protein production in E. coli as it may eliminate the translation initiation as the rate-limiting step of the translation process (Nieuwkoop et al 2019).…”