Conclusion
PTM-enzymes enable many advances in chemical biology, biotechnology, and biomedicine. Although many technologies exist to investigate PTM-enzymes, S. cerevisiae is one of the most versatile organisms for PTM-enzyme biochemical assays due to its genetic tractability and comprehensive synthetic biology toolbox. This success is contingent on organelle sequestration strategies where PTM-enzyme activities can be tied to a measurable signal. Here, we described three general approaches to measure the activities of PTM-enzymes and profile and engineer their substrate specificity in a high-throughput manner.
Modified YSD approaches have been used to engineer the substrate specificity of bond-forming enzymes. The sortase examples showcase both the power and limitations of YSD in this regard. Of note, the A2M bait-and-capture method may provide a significant advance for engineering proteases on the yeast surface. It will be interesting to test this method on narrowing the substrate specificity of promiscuous and toxic proteases. Furthermore, protease-dependent synthetic protein circuits that drive a transcriptional output are quite useful for protease substrate specificity engineering. These approaches often allow one to engineer a PTM-enzyme in the same context of its application. Lastly, sequestration in the ER, particularly with YESS, has useful advantages over YSD and cytosolic sequestration, beyond YESS’ ability to operate under multiple catalytic turnovers. The versatility of YESS in specificity profiling, engineering and inhibitor screening and its growing PTM-enzyme compatibility places it at the forefront of technologies. The YESS system allows the user to easily introduce one or more counterselection substrates without drastically changing the system’s performance. However, a unique challenge in protease engineering in yeast in general is to include counterselection substrates in a higher stoichiometric ratio than the selection substrate to impose higher selection stringencies. Expressing additional copies of counterselection substrates may not match competition assays achieved by exogenous addition (Podracky et al., 2021) or through orthogonal transcriptional nodes (Blum et al., 2021). In this vein, modifications that leverage ER retention signal binding affinities may provide a solution to this limitation. In conclusion, using yeast as a chassis for PTM-enzyme high-throughput assays significantly lowers the barrier to understanding how these enzymes work and how to reprogram them. In the coming years, we expect to see more PTM-enzymes integrate systems such as YESS, including protein arginine methyltransferases, formylglcine-generating enzymes, and sortases, as well as more complicated protein circuits that enable reprogramming through complex protein interactions. Finally, the integration of high-throughput sequence function data with machine learning lends itself perfectly to the yeast assays described here and will continue to push this field forward.