4.7 Article

EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling

Journal

JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 62, Issue 3, Pages 647-655

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.1c01424

Keywords

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Funding

  1. Vanderbilt University
  2. National Science Foundation [TGBIO200057.61]

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Molecular simulations have been widely used in understanding enzyme catalysis and designing new enzymes. However, manual operation is usually required throughout the simulation process, making it challenging to simulate enzymes in a high-throughput manner. In this work, a Python software called EnzyHTP was developed to automate the entire workflow of enzyme modeling, improving the efficiency of computational 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 functionenhancing 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.

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