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.