EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling

Molecular
simulations, including quantum mechanics (QM), molecular
mechanics (MM), and multiscale QM/MM modeling, have been extensively
applied to understand the mechanism of enzyme catalysis and to design
new enzymes. However, molecular simulations typically require specialized,
manual operation ranging from model construction to data analysis
to complete the entire life cycle of enzyme modeling. The dependence
on manual operation makes it challenging to simulate enzymes and enzyme
variants in a high-throughput fashion. In this work, we developed
a Python software, EnzyHTP, to automate molecular model construction,
QM, MM, and QM/MM computation, and analyses of modeling data for enzyme
simulations. To test the EnzyHTP, we used fluoroacetate dehalogenase
(FAcD) as a model system and simulated the enzyme interior electrostatics
for 100 FAcD mutants with a random single amino acid substitution.
For each enzyme mutant, the workflow involves structural model construction,
1 ns molecular dynamics (MD) simulations, and quantum mechanical calculations
in 100 MD-sampled snapshots. The entire simulation workflow for 100
mutants was completed in 7 h with 10 GPUs and 160 CPUs. EnzyHTP improves
the efficiency of computational enzyme modeling, setting a basis for
high-throughput identification of function-enhancing enzymes and enzyme
variants. The software is expected to facilitate the fundamental understanding
of catalytic origins across enzyme families and to accelerate the
optimization of biocatalysts for non-native substrates.