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 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.

ARMer: A Python Library for Adaptive Resource Allocation in High-Throughput Workflow

“…We employed fluoroacetate dehalogenase (FAcD) 23-27 as a model system and conducted a high-throughput workflow of enzyme simulations using EnzyHTP. 16 The workflow consists of four sequential sub-tasks, namely, 1) mutant structure construction, 2) MD simulation, 3) QM calculation, and 4) post-analysis. Notably, the model system and workflow have been applied in our previous work to test the throughput capability of EnzyHTP.…”

“…Notably, the model system and workflow have been applied in our previous work to test the throughput capability of EnzyHTP. 16 However, unlike the previous workflow that sampled 100 variants with minimalist resource cost, the current workflow only tested one enzyme variant (i.e, K83D) with MD and QM simulations set up to meet resource demand in actual computational research (Supporting Information .zip).…”

“…In modern computational chemistry, high-throughput computation emerges as a new paradigm to address challenges in studying reaction mechanisms, [1][2][3] screening catalysts, [4][5][6][7][8] designing functional materials, [9][10][11][12][13] discovering drug candidates, 14,15 and modeling enzymes [16][17][18] . With the advent of machine learning, these workflows allow facile collection of molecular features, or even provide high-accuracy computational data for model training.…”

“…As such, the workflow needs to allocate different computing resources for different sub-tasks. For example, a workflow of enzyme modeling (e.g., EnzyHTP 16 ) needs to perform CPU-based structure model construction, GPUbased molecular dynamics (MD) simulation, CPU-based quantum mechanics (QM) calculation, and CPU-based data analysis. The diverse requirement of computing resources presents a challenge to operate high-throughput workflow in high-performance computing (HPC) clusters where a job scheduler is used to manage resource allocation.…”

LassoHTP: a High-throughput Computational Tool for Lasso Peptide Structure Construction and Modeling

“…The modular architecture ensures flexibility in application, which is similar to EnzyHTP, a software we developed for high-throughput enzyme modeling. 26 Any single module can be independently executed to build, modify, or model a lasso peptide. Or, the three modules can be sequentially operated in an automatic workflow to convert user-defined lasso peptide sequences to structural ensembles.…”