Directed evolution has transformed protein engineering
offering
a path to rapid improvement of protein properties. Yet, in practice
it is limited by the hyper-astronomic protein sequence search space,
and approaches to identify mutagenic hot spots, i.e., locations where
mutations are most likely to have a productive impact, are needed.
In this perspective, we categorize and discuss recent progress in
the experimental approaches (broadly defined as structural, bioinformatic,
and dynamic) to hot spot identification. Recent successes in harnessing
protein dynamics and machine learning approaches provide new opportunities
for the field and will undoubtedly help directed evolution reach its
full potential.