An engineered, in vitro translation system with the ability to efficiently translate a broad range of unnatural amino acids will allow scientists to easily manipulate peptides and proteins to address the growing need for new antibiotics, cancer treatments, and alternatives for naturally occurring medicinal compounds. Additionally, incorporating unnatural amino acids into peptides has utility in protein research, nanostructure modification, and polymer design. Of the several methods currently used to manufacture peptides containing unnatural amino acids (cell culture, solid-phase synthesis, or liquid-phase synthesis), all have distinct drawbacks and limitations. Our system avoids problems involved with in vivo techniques such as host organism viability, feedback mechanisms, and product degradation or modification, as well as challenges involved with chemical protocols including caustic solvents, limited peptide length, and low yields. We propose a system of in vitro translation that exploits the cellular mechanism used for translation and involves engineering aspects of it (i.e., elongation factors and transfer-RNA) to efficiently produce peptides/ proteins that incorporate unnatural amino acids. We have demonstrated that computational analysis can be harnessed to design protein variants with diverse phenotypes including recognition and acceptance of unnatural amino acids. Our protein library is designed to probe the association between EF-Tu and aminoacylated tRNAs in order to efficiently deliver tRNAs acylated with unnatural amino acids to the ribosome. This system would allow researchers to design therapeutic proteins which utilize the benefits that unnatural amino acids offer including altered function, resistance to proteolysis, and altered solubility (e.g., membrane permeability). Overall, it is vital for the advancement of protein and pharmaceutical research that an efficient method be developed to incorporate unnatural amino acids into peptides.

Translating unnatural amino acids with phenotypically-diverse computationally-engineered EF-Tu variants

253rd National Meeting (2017)

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