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1 Ontario Cancer Institute and Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 2M9, Canada
2 Banting and Best Department of Medical Research and Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario M5G 1L6, Canada
Reprint requests to: Aled M. Edwards, Banting and Best Department of Medical Research and Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario M5G 1L6, Canada; e-mail: aled.edwards{at}utoronto.ca; fax: (416) 978-8528.
Low in vivo solubility of recombinant proteins expressed in Escherichia coli can seriously hinder the purification of structural samples for large-scale proteomic NMR and X-ray crystallography studies. Previous results from our laboratory have shown that up to one half of all bacterial and archaeal proteins are insoluble when overexpressed in E. coli. Although a number of strategies may be used to increase in vivo protein solubility, there are no generally applicable methods, and the expression of each insoluble recombinant protein must be individually optimized. For this reason, we have tested a generic denaturation/refolding protein purification procedure to assess the number of structural samples that could be generated by using this methodology. Our results show that a denaturation/refolding protocol is appropriate for many small proteins (
18 kD) that are normally soluble in vivo. In addition, refolding the purified proteins by using dialysis against a single buffer allowed us to obtain soluble protein samples of 58% of small proteins that were found in the insoluble fraction in vivo, and 10% of the initial number of proteins provided good heteronuclear single quantum coherence (HSQC) NMR spectra. We conclude that a denaturation/refolding protocol is an efficient way to generate structural samples for high-throughput studies of small proteins.
Keywords: Protein structure/folding; protein solubility; protein purification; NMR spectroscopy; circular dichroism spectroscopy; structural proteomics
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