Over the last 25 years, protein engineers have developed
an impressive
collection of optical tools to interface with biological systems:
indicators to eavesdrop on cellular activity and actuators to poke
and prod native processes. To reach the performance level required
for their downstream applications, protein-based tools are usually
sculpted by iterative rounds of mutagenesis. In each round, libraries
of variants are made and evaluated, and the most promising hits are
then retrieved, sequenced, and further characterized. Early efforts
to engineer protein-based optical tools were largely manual, suffering
from low throughput, human error, and tedium. Here, we describe approaches
to automating the screening of libraries generated as colonies on
agar, multiwell plates, and pooled populations of single-cell variants.
We also briefly discuss emerging approaches for screening, including
cell-free systems and machine learning.