Codon pair optimization (CPO): a software tool for synthetic gene design based on codon pair bias to improve the expression of recombinant proteins in Pichia pastoris

Background Codon optimization is a common method to improve protein expression levels in Pichia pastoris and the current strategy is to replace rare codons with preferred codons to match the codon usage bias. However, codon-pair contexts have a profound effect on translation efficiency by influencing both translational elongation rates and accuracy. Until now, it remains untested whether optimized genes based on codon pair bias results in higher protein expression levels compared to codon usage bias. Results In this study, an algorithm based on dynamic programming was introduced to develop codon pair optimization (CPO) which is a software tool to provide simple and efficient codon pair optimization for synthetic gene design in Pichia pastoris. Two reporters (MT1-MMP E2C6 and ADAM17 A9B8 scFvs) were employed to test the effects of codon pair bias and CPO optimization on their protein expression levels. Four variants of MT1-MMP E2C6 and ADAM17 A9B8 for each were generated, one variant with the best codon-pair context, one with the worst codon-pair context, one with unbiased codon-pair context, and another optimized based on codon usage. The expression levels of variants with the worst codon-pair context were almost undetectable by Western blot and the variants with the best codon-pair context were expressed well. The expression levels on MT1-MMP E2C6 and ADAM17 A9B8 were more than five times and seven times higher in the optimized sequences based on codon-pair context compared to that based on codon usage, respectively. The results indicated that the codon-pair context-based codon optimization is more effective in enhancing expression of protein in Pichia pastoris. Conclusions Codon-pair context plays an important role on the protein expression in Pichia pastoris. The codon pair optimization (CPO) software developed in this study efficiently improved the protein expression levels of exogenous genes in Pichia pastoris, suggesting gene design based on codon pair bias is an alternative strategy for high expression of recombinant proteins in Pichia pastoris.

Critical points worthy of consideration in the soluble expression of recombinant proteins in Escherichia coli: the accuracy of the solubility prediction tools versus experimental results

Purpose: Recombinant proteins have become increasingly important items in research and industry. Due to its low cost, high yield and rapid growth rate, Escherichia coli (E. coli) is the first choice as host for the production of recombinant proteins. The expression of recombinant proteins in E. coli systems often result in inclusion bodies lacking proper folding and structure. In silico bioinformatics prediction tools may be promising in optimal expression of soluble recombinant proteins. Materials and methods: In this review, we aimed at making critical recommendations on how to improve the soluble expression of recombinant proteins. Furthermore, we compared the solubility of recombinant proteins using bioinformatics prediction tools versus experimental results. Data were analyzed using SPSS software. Results: Some recommendations worthy of consideration in gene design and expression were reminded. The results of a comparison between bioinformatics and experimental methods revealed that no significant coordination existed. RPSP and SOLpro showed higher sensitivity (43.5% and 56.5%, respectively) and specificity (52.9% and 47.1%, respectively), when compared to FoldIndex and PSoL. The results from p-value and roc curve indicated the effect of MW, helix percentage and aliphatic index on protein solubility (p-value< 0.05). Conclusions: This review discusses efficient expression of soluble recombinant proteins. The bioinformatics prediction tools were examined for their sensitivity and specificity. MW, helix percentage and aliphatic parameters should be considered in gene design.