A-Factor Analogues Containing Alkyne- and Azide-Functionalized Isoprenoids Are Efficiently Enzymatically Processed and Retain Wild-Type Bioactivity

Protein prenylation is a post-translational modification that involves the addition of one or two isoprenoid groups to the C-terminus of selected proteins using either farnesyl diphosphate or geranylgeranyl diphosphate. Three crucial enzymatic steps are involved in the processing of prenylated proteins to yield the final mature product. The farnesylated dodecapeptide, a-factor, is particularly useful for studies of protein prenylation because it requires the identical three-step process to generate the same C-terminal farnesylated cysteine methyl ester substructure present in larger farnesylated proteins. Recently, several groups have developed isoprenoid analogs bearing azide and alkyne groups that can be used in metabolic labeling experiments. Those compounds have proven useful for profiling prenylated proteins and also show great promise as tools to study how the levels of prenylated proteins vary in different disease models. Herein, we describe the preparation and use of prenylated a-factor analogs, and precursor peptides, to investigate two key questions. First, a-factor analogues containing modified isoprenoids were prepared to evaluate whether the non-natural lipid group interferes with the biological activity of the a-factor. Second, a-factor-derived precursor peptides were synthesized to evaluate whether they can be efficiently processed by the yeast proteases Rce1 and Ste24 as well as the yeast methyltransferase Ste14 to yield mature a-factor analogues. Taken together, the results reported here indicate that metabolic labeling experiments with azide- and alkyne-functionalized isoprenoids can yield prenylated products that are fully processed and biologically functional. Overall, these observations suggest that the isoprenoids studied here that incorporate bio-orthogonal functionality can be used in metabolic labeling experiments without concern that they will induce undesired physiological changes that may complicate data interpretation.