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. Accounting for NQE through molecular force field corrections circumvents the need for explicit computationally expensive PIMD simulations in accurate calculations of the properties of chemical and biological systems. The accuracy and locality of pairwise NN NQE corrections indicate that this approach could be applicable to complex heterogeneous systems, such as proteins.

A Hierarchical Approach to Predict Conformation-Dependent Histidine Protonation States in Stable and Flexible Proteins.

Solution acidity measured by pH is an important environmental factor that affects protein structure. It influences the protonation state of protein residues, which in turn may be coupled to protein conformational changes, unfolding, and ligand binding. It remains difficult to compute and measure the p Ka of individual residues, as well as to relate them to pH-dependent protein transitions. This paper presents a hierarchical approach to compute the p Ka of individual protonatable residues, specifically histidines, coupled with underlying structural changes of a protein. A fast and efficient free energy perturbation (FEP) algorithm has also been developed utilizing a fast implementation of standard molecular dynamics (MD) algorithms. Specifically, a CUDA version of the AMBER MD engine is used in this paper. Eight histidine p Ka's are computed in a diverse set of pH stable proteins to demonstrate the proposed approach's utility and assess the predictive quality of the AMBER FF99SB force field. A reference molecule is carefully selected and tested for convergence. A hierarchical approach is used to model p Ka's of the six histidine residues of the diphtheria toxin translocation domain (DTT), which exhibits a diverse ensemble of individual conformations and pH-dependent unfolding. The hierarchical approach consists of first sampling equilibrium conformational ensembles of a protein with protonated and neutral histidine residues via long equilibrium MD simulations (Flores-Canales, J. C.; et al. bioRxiv, 2019, 572040). A clustering method is then used to identify sampled protein conformations, and p Ka's of histidines in each protein conformation are computed. Finally, an ensemble averaging formalism is developed to compute weighted average histidine p Ka's. These can be compared with an apparent experimentally measured p Ka of the DTT protein and thus allows us to propose a mechanism of pH-dependent unfolding of the DTT protein.

AMPA Receptor Noncompetitive Inhibitors Occupy a Promiscuous Binding Site.

Noncompetitive inhibitors of AMPA receptors have attracted interest in recent years as antiepileptic drugs. However, their development is hindered by a lack of detailed understanding of the protein-inhibitor interaction mechanisms. Recently, structures of AMPA receptor complexes with the structurally dissimilar, noncompetitive, small-molecule inhibitors pyridone perampanel (PMP), GYKI 53655 (GYKI), and CP 465022 (CP) were resolved, revealing that all three share a common binding site. However, due to the low resolution of the ligands, their exact binding modes and protein-ligand interactions remain ambiguous and insufficiently detailed. We carried out molecular dynamics (MD) simulations on X-ray-resolved and docked AMPA receptor complexes, including thermodynamic integration (TI) to compute ligand binding constants, in order to investigate the inhibitor binding modes in detail and identify key protein-ligand interaction mechanisms. Our analysis and simulations show that the ligand binding pocket at the interface of the receptor's transmembrane domain exhibits features also found in the binding pockets of the multidrug-resistance proteins. The inhibitors bind to such promiscuous pockets by forming multiple weak contacts, while the large, flexible pocket undergoes adjustments to accommodate structurally different ligands in different orientations. TI was able to identify a specific more favorable binding mode for GYKI, while PMP, which has a symmetric ring structure, produced several comparable poses indicating that it may bind in several orientations.