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Design parameters to control synthetic gene expression in Escherichia coli.

Welch, Govindarajan, Ness, Villalobos, Gurney, Minshull, Gustafsson.
ATUM (DNA2.0), OncoMed Pharmaceuticals Inc.
PloS One 2009, 4(9):e7002
Gene names: DNA polymerase, single-chain antibody fragment (scFv). Host systems: Escherichia coli BL21(DE3)[Bacterial]. Gene species: Escherichia coli[Bacterial], Phage. Optimized: Yes. Protein activity: Yes. Vectors: pET24a.
Abstract: Production of proteins as therapeutic agents, research reagents and molecular tools frequently depends on expression in heterologous hosts. Synthetic genes are increasingly used for protein production because sequence information is easier to obtain than the corresponding physical DNA. Protein-coding sequences are commonly re-designed to enhance expression, but there are no experimentally supported design principles.To identify sequence features that affect protein expression we synthesized and expressed in E. coli two sets of 40 genes encoding two commercially valuable proteins, a DNA polymerase and a single chain antibody. Genes differing only in synonymous codon usage expressed protein at levels ranging from undetectable to 30% of cellular protein. Using partial least squares regression we tested the correlation of protein production levels with parameters that have been reported to affect expression. We found that the amount of protein produced in E. coli was strongly dependent on the codons used to encode a subset of amino acids. Favorable codons were predominantly those read by tRNAs that are most highly charged during amino acid starvation, not codons that are most abundant in highly expressed E. coli proteins. Finally we confirmed the validity of our models by designing, synthesizing and testing new genes using codon biases predicted to perform well.The systematic analysis of gene design parameters shown in this study has allowed us to identify codon usage within a gene as a critical determinant of achievable protein expression levels in E. coli. We propose a biochemical basis for this, as well as design algorithms to ensure high protein production from synthetic genes. Replication of this methodology should allow similar design algorithms to be empirically derived for any expression system.
Comments: Systematic identification and quantification of the relevant variables for maximizing protein expression from synthetic genes. The rigorous data analysis is used to develop a consistent and reliable optimization algorithm for the design of synthetic genes.