EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling

Abstract

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 post-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 simulations, and quantum mechnical calculations in 100 MD-sampled snapshots. The entire simulation workflow for 100 mutants was completed in 7 hours with 10 GPUs and 160 CPUs. EnzyHTP is expected to improve the efficiency and reproducibility of computational enzyme, facilitate the fundamental understanding of catalytic origins across enzyme families, and accelerate the optimization of biocatalysts for non-native substrate transformation.