EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation

Directed evolution facilitates enzyme engineering via
iterative
rounds of mutagenesis. Despite the wide applications of high-throughput
screening, building “smart libraries” to effectively
identify beneficial variants remains a major challenge in the community.
Here, we developed a new computational directed evolution protocol
based on EnzyHTP, a software that we have previously reported to automate
enzyme modeling. To enhance the throughput efficiency, we implemented
an adaptive resource allocation strategy that dynamically allocates
different types of computing resources (e.g., GPU/CPU) based on the
specific need of an enzyme modeling subtask in the workflow. We implemented
the strategy as a Python library and tested the library using fluoroacetate
dehalogenase as a model enzyme. The results show that compared to
fixed resource allocation where both CPU and GPU are on-call for use
during the entire workflow, applying adaptive resource allocation
can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed
a computational directed evolution protocol under the framework of
adaptive resource allocation. The workflow was tested against two
rounds of mutational screening in the directed evolution experiments
of Kemp eliminase (KE07) with a total of 184 mutants. Using folding
stability and electrostatic stabilization energy as computational
readout, we identified all four experimentally observed target variants.
Enabled by the workflow, the entire computation task (i.e., 18.4 μs
MD and 18,400 QM single-point calculations) completes in 3 days of
wall-clock time using ∼30 GPUs and ∼1000 CPUs.