A de novo protein confers copper resistance in Escherichia coli.

UNLABELLED: To survive environmental challenges, biological systems rely on proteins that were selected by evolution to function in particular cellular and conditional settings. With the advent of protein design and synthetic biology, it is now possible to construct novel proteins that are not biased by eons of selection in natural hosts. The availability of these sequences prompts us to ask whether natural biological organisms can use naïve-non-biological-proteins to enhance fitness in stressful environments. To address this question, we transformed a library of DNA sequences encoding ∼1.5 × 10(6) binary patterned de novo proteins into E. coli, and selected for sequences that enable growth in concentrations of copper that would otherwise be toxic. Several novel sequences were discovered, and one of them, called Construct K (ConK), was studied in detail. Cells expressing ConK accumulate approximately 50% less copper than control cells. The function of ConK does not involve an oxidase, nor does it require two of the best characterized copper efflux systems. However, the ability of ConK to rescue cells from toxic concentrations of copper does require an active proton motive force. Further selections for growth in higher concentrations of copper led to the laboratory evolution of variants of ConK with enhanced levels of activity in vivo. These studies demonstrate that novel proteins, unbiased by evolutionary history in the natural world, can enhance the fitness of biological systems. SYNOPSIS: Living systems evolve to adapt to potentially lethal environmental changes. This normally involves repurposing existing genetic information (i.e. sequences that were selected by billions of years of evolution). Here we show that a completely de novo protein, not derived from nature, can enable E. coli cells to grow in otherwise toxic concentrations of copper, demonstrating that living systems also have the capacity to incorporate and protopurpose entirely novel genetic information.