Improving membrane protein expression by optimizing integration efficiency
The heterologous overexpression of integral membrane proteins in Escherichia coli often yields insufficient quantities of purifiable protein for applications of interest. The current study leverages a recently demonstrated link between co-translational membrane integration efficiency and protein expression levels to predict protein sequence modifications that improve expression. Membrane integration efficiencies, obtained using a coarse-grained simulation approach, robustly predicted effects on expression of the integral membrane protein TatC for a set of 140 sequence modifications, including loop-swap chimeras and single-residue mutations distributed throughout the protein sequence. Mutations that improve simulated integration efficiency were 4-fold enriched with respect to improved experimentally observed expression levels. Furthermore, the effects of double mutations on both simulated integration efficiency and experimentally observed expression levels were cumulative and largely independent, suggesting that multiple mutations can be introduced to yield higher levels of purifiable protein. This work provides a foundation for a general method for the rational overexpression of integral membrane proteins based on computationally simulated membrane integration efficiencies.
© 2017 The American Society for Biochemistry and Molecular Biology. Received August 18, 2017; Accepted September 16, 2017; First Published on September 16, 2017. This work was supported by NIGMS, National Institutes of Health, Grant 1R01GM125063 (to T. F. M. and W. M. C.). Work in the Clemons laboratory was supported by National Institutes of Health Pioneer Award 5DP1GM105385 (to W. M. C.), funds from Caltech's Center for Environmental Microbial Interactions, and NRSA, National Institutes of Health, Training Grant 5T32GM07616 (to S. S. M.). Work in the Miller group is supported in part by Office of Naval Research Grant N00014-10-1-0884. The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Computational resources were provided by the National Energy Research Scientific Computing Center (NERSC), a United States Department of Energy Office of Science User Facility (DE-AC02-05CH11231), and the Extreme Science and Engineering Discovery Environment (XSEDE) (55), which is supported by National Science Foundation Grant ACI-1053575.
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