An efficient methodology has been developed to quantify water energetics by analysis of explicit solvent molecular simulations of organic and biomolecular systems. The approach, grid cell theory (GCT), relies on a discretization of the cell theory methodology on a three-dimensional grid to spatially resolve the density, enthalpy, and entropy of water molecules in the vicinity of solute(s) of interest. Entropies of hydration are found to converge more efficiently than enthalpies of hydration. GCT predictions of free energies of hydration on a data set of small molecules are strongly correlated with thermodynamic integration predictions. Agreement with the experiment is comparable for both approaches. A key advantage of GCT is its ability to provide from a single simulation insightful graphical analyses of spatially resolved components of the enthalpies and entropies of hydration.
Drug-target residence time, the length of time for which a small molecule stays bound to its receptor target, has increasingly become a key property for optimization in drug discovery programs. However, its in silico prediction has proven difficult. Here we describe a method, using atomistic ensemble-based steered molecular dynamics (SMD), to observe the dissociation of ligands from their target G protein-coupled receptor in a time scale suitable for drug discovery. These dissociation simulations accurately, precisely, and reproducibly identify ligand-residue interactions and quantify the change in ligand energy values for both protein and water. The method has been applied to 17 ligands of the A 2A adenosine receptor, all with published experimental kinetic binding data. The residues that interact with the ligand as it dissociates are known experimentally to have an effect on binding affinities and residence times. There is a good correlation (R 2 = 0.79) between the computationally calculated change in waterligand interaction energy and experimentally determined residence time. Our results indicate that ensemble-based SMD is a rapid, novel, and accurate semi-empirical method for the determination of drug-target relative residence time.
Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38α MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are observed. Protocols that vary in their use of restraints on protein and ligand atoms were compared to determine the influence of protein-ligand flexibility on computed water structure and energetics, and to assess protocols for routine analyses of protein-ligand complexes. The GCT-derived binding affinities correctly reproduce experimental trends, but the magnitude of the predicted changes in binding affinities is exaggerated with respect to results from a previous Monte Carlo Free Energy Perturbation study. Breakdown of the GCT water free energies into enthalpic and entropic components indicates that enthalpy changes dominate the observed variations in energetics. In EGFR kinase GCT analyses revealed that replacement of a pyrimidine by a cyanopyridine perturbs water energetics up three hydration shells away from the ligand.
The reliable and precise evaluation of receptor–ligand interactions and pair‐interaction energy is an essential element of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been used to accelerate QM calculations, and by combining FMO with the density‐functional tight‐binding (DFTB) method we are able to decrease computational cost 1000 times, achieving results in seconds, instead of hours. We have applied FMO‐DFTB to three different GPCR–ligand systems. Our results correlate well with site directed mutagenesis data and findings presented in the published literature, demonstrating that FMO‐DFTB is a rapid and accurate means of GPCR–ligand interactions. © 2017 Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.
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