Removal of DnaK contamination during fusion protein purifications

https://doi.org/10.1016/S1046-5928(02)00024-4Get rights and content

Abstract

The use of fusion proteins for recombinant protein expression in Escherichia coli has become popular because the carrier increases protein solubility, standardizes expression levels, and facilitates purification of the fusion products. However, we have observed that the peptide regions that fuse the carrier to the protein of interest bind E. coli Hsp70 molecular chaperones (DnaK) depending on their amino acid composition, resulting in an unwanted contamination during protein purification. Here we describe an approach that helps to circumvent this unwanted contamination. First, the appropriate amino acids surrounding and comprising the cloning site are chosen by using a software based on an algorithm already developed to decrease to a minimum the propensity of the fusion protein to bind DnaK. Second, DnaK contamination is significantly reduced by washing the fusion protein bound to the purification resin with MgATP plus soluble denatured E. coli proteins before elution. The approach can also be applied to eliminate other molecular chaperones.

Section snippets

DnaK binding site prediction

Identification of possible DnaK binding sites on the linker regions of the different vectors was performed as described previously [2], using the algorithm of Rüdiger et al. [3]. These authors have identified DnaK binding sites by screening 4360 cellulose-bound peptides scanning the sequences of 37 biologically relevant proteins [3]. The algorithm attributes overall statistical energy contributions (ΔΔGK) to peptides of 13 adjacent amino acids. The lower the value obtained for a specific

Results and discussion

We have analyzed the connector peptide regions of several pET and pGEX vectors using a predicting algorithm for DnaK binding motifs within protein sequences [3]. This algorithm analyzes 13-mer peptides consisting in a core of five residues and two flanking regions of four residues each. The algorithm attributes overall statistical energy contributions (ΔΔGK) to the 13 adjacent amino acid windows. The lower the value obtained for a specific segment, the higher the predicted affinity for DnaK.

Acknowledgements

This work was supported by grants from CONICET, UNR, and Foncyt, Argentina. EAC is member of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina). D.V.R. is a Fellow of the same institution.

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