Evolutionary synthetic biology: structure/function relationships within the protein translation system
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Production of mutant biological molecules for understanding biological principles or as therapeutic agents has gained considerable interest recently. Synthetic genes are today being widely used for production of such molecules due to the substantial decrease in the costs associated with gene synthesis technology. Along one such line, we have engineered tRNA genes in order to dissect the effects of G:U base-pairs on the accuracy of the protein translation machinery. Our results provide greater detail into the thermodynamic interactions between tRNA molecules and an Elongation Factor protein (termed EF-Tu in bacteria and eEF1A in eukaryotes) and how these interactions influence the delivery of aminoacylated tRNAs to the ribosome. We anticipate that our studies not only shed light on the basic mechanisms of molecular machines but may also help us to develop therapeutic or novel proteins that contain unnatural amino acids. Further, the manipulation of the translation machinery holds promise for the development of new methods to understand the origins of life. Along another line, we have used the power of synthetic biology to experimentally validate an evolutionary model. We exploited the functional diversity contained within the EF-Tu/eEF1A gene family to experimentally validate the model of evolution termed ‘heterotachy’. Heterotachy refers to a switch in a site’s mutational rate class. For instance, a site in a protein sequence may be invariant across all bacterial homologs while that same site may be highly variable across eukaryotic homologs. Such patterns imply that the selective constraints acting on this site differs between bacteria and eukaryotes. Despite intense efforts and large interest in understanding these patterns, no studies have experimentally validated these concepts until now. In the present study, we analyzed EF-Tu/eEF1A gene family members between bacteria and eukaryotes to identify heterotachous patterns (also called Type-I functional divergence). We applied statistical tests to identify sites possibly responsible for biomolecular functional divergence between EF-Tu and eEF1A. We then synthesized protein variants in the laboratory to validate our computational predictions. The results demonstrate for the first time that the identification of heterotachous sites can be specifically implicated in functional divergence among homologous proteins. In total, this work supports an evolutionary synthetic biology paradigm that in one direction uses synthetic molecules to better understand the mechanisms and constraints governing biomolecular behavior while in another direction uses principles of molecular sequence evolution to generate novel biomolecules that have utility for industry and/or biomedicine.