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.

Vibrational spectroscopy provides a powerful tool to probe the structure and dynamics of nucleic acids because specific normal modes, in particular the base carbonyl stretch modes, are highly sensitive to the hydrogen bonding patterns and stacking configurations in these biomolecules. In this work, we develop vibrational frequency maps for the C=O and C=C stretches in nucleobases that allow the calculations of their site frequencies directly from molecular dynamics simulations. We assess the frequency maps by applying them to nucleobase derivatives in aqueous solutions and nucleosides in organic solvents, and demonstrate that the predicted infrared spectra are in good agreement with experimental measurements. The frequency maps can be readily used to model the linear and non-linear vibrational spectroscopy of nucleic acids and elucidate the molecular origin of the experimentally observed spectral features.

Vibrational spectroscopy, in particular infrared spectroscopy, has been widely used to probe the three-dimensional structures and conformational dynamics of nucleic acids. As commonly used chromophores, the C=O and C=C stretch modes in the nucleobases exhibit distinct spectral features for different base pairing and stacking configurations. To elucidate the origin of their structural sensitivity, in this work, we develop transition charge coupling (TCC) models that allow one to efficiently calculate the interactions or couplings between the C=O and C=C chromophores based on the geometric arrangements of the nucleobases. To evaluate their performances, we apply the TCC models to DNA and RNA oligonucleotides with a variety of secondary and tertiary structures and demonstrate that the predicted couplings are in quantitative agreement with the reference values. We further elucidate how the interactions between the paired and stacked bases give rise to characteristic IR absorption peaks and show that the TCC models provide more reliable predictions of the coupling constants as compared to the transition dipole coupling scheme. The TCC models, together with our recently developed through-bond coupling constants and vibrational frequency maps, provide an effective theoretical strategy to model the vibrational Hamiltonian, and hence the vibrational spectra of nucleic acids in the base carbonyl stretch region directly from atomistic molecular simulations.