Comparative Antibacterial Analysis of the Anthraquinone Compounds Based on the AIM Theory and Molecular Docking Analysis

Hydroxyanthraquinones and anthraquinone glucoside derivatives are always considered as the active antibacterial components. In the present text, a comprehensive comparison and analysis of these compounds were performed for their structure characteristics and antibacterial effect by applying quantum chemical calculations, atoms in molecules theory and molecular docking procedure. The molecular geometric configuration, electrostatic potential, the frontier orbital energies and topological properties were analyzed. Once glucose ring is introduced into the hydroxyanthraquinone rings, almost all of the positive molecular potentials are distributed among the hydroxyl hydrogen atoms of the glucose rings. The anthraquinone glucoside compounds have generally higher intermolecular binding energies than the corresponding aglycones due to the strong interaction between the glucose rings and the surrounding amino acids. Once glucoside ring is introduced into the emodin, low electron density ρ(r) and positive Laplacian value of the O-H bond are the evidences of the highly polarized and covalently decreased bonding interactions. The type of carboxyl, hydroxyl, hydroxylmethyl groups on phenyl ring and the substituent glucose rings are important to the interactions with the topoisomerase type II enzyme DNA gyrase B.

Transferability of Molecular Potentials for 2D Molybdenum Disulphide

An ability of different molecular potentials to reproduce the properties of 2D molybdenum disulphide polymorphs is examined. Structural and mechanical properties, as well as phonon dispersion of the 1H, 1T and 1T’ single-layer MoS2 (SL MoS2) phases, were obtained using density functional theory (DFT) and molecular statics calculations (MS) with Stillinger-Weber, REBO, SNAP and ReaxFF interatomic potentials. Quantitative systematic comparison and discussion of the results obtained are reported.

Transferability of Molecular Potentials for 2D Molybdenum Disulphide

An ability of different molecular potentials to reproduce the properties of 2D molybdenum disulphide polymorphs is examined. Structural and mechanical properties, as well as phonon dispersion of the 2H, 1T and 1T’ single-layer MoS2 (SL MoS2) phases, were obtained using density functional theory (DFT) and molecular statics calculations (MS) with Stillinger-Weber, REBO, SNAP, and ReaxFF interatomic potentials. Quantitative systematic comparison and discussion of the results obtained are reported.

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

<p>Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. </p>

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

<p>Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. </p>