Spectroscopic and computational studies of a bifunctional iron- and 2-oxoglutarate dependent enzyme, AsqJ.

Iron and 2-oxoglutarate dependent (Fe/2OG) enzymes exhibit an exceedingly broad reaction repertoire. The most prevalent reactivity is hydroxylation, but many other reactivities have also been discovered in recent years, including halogenation, desaturation, epoxidation, endoperoxidation, epimerization, and cyclization. To fully explore the reaction mechanisms that support such a diverse reactivities in Fe/2OG enzyme, it is necessary to utilize a multi-faceted research methodology, consisting of molecular probe design and synthesis, in vitro enzyme assay development, enzyme kinetics, spectroscopy, protein crystallography, and theoretical calculations.

Neural Network Corrections to Intermolecular Interaction Terms of a Molecular Force Field Capture Nuclear Quantum Effects in Calculations of Liquid Thermodynamic Properties.

We incorporate nuclear quantum effects (NQE) in condensed matter simulations by introducing short-range neural network (NN) corrections to the ab initio fitted molecular force field ARROW. Force field NN corrections are fitted to average interaction energies and forces of molecular dimers, which are simulated using the Path Integral Molecular Dynamics (PIMD) technique with restrained centroid positions. The NN-corrected force field allows reproduction of the NQE for computed liquid water and methane properties such as density, radial distribution function (RDF), heat of evaporation (HVAP), and solvation free energy.

Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions.

We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a corresponding neural network may be used to economically improve the representation of torsional degrees of freedom in any force field.

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g.