Application of the linear interaction energy method (LIE) to estimate the binding free energy values of Escherichia coli wild-type and mutant arginine repressor C-terminal domain (ArgRc)–l-arginine and ArgRc–l-citrulline protein–ligand complexes

Molecular recognition of metabotropic glutamate receptor type 1 (mGluR1): synergistic understanding with free energy perturbation and linear response modeling

Metabotropic glutamate receptors (mGluRs) constitute an important family of the G-protein coupled receptors. Due to their widespread distribution in the central nervous system (CNS), these receptors are attractive candidates for understanding the molecular basis of various cognitive processes as well as for designing inhibitors for relevant psychiatric and neurological disorders. Despite many studies on drugs targeting the mGluR receptors to date, the molecular level details on the ligand binding dynamics still remain unclear. In this study, we performed in silico experiments for mGluR1 with 29 different ligands including known synthetic agonists and antagonists as well as natural amino acids. The ligand-receptor binding affinities were estimated by the use of atomistic simulations combined with the mathematically rigorous, Free Energy Perturbation (FEP) method, which successfully recognized the native agonist l-glutamate among the highly favorable binders, and also accurately distinguished antagonists from agonists. Comparative contact analysis also revealed the binding mode differences between natural and non-natural amino acid-based ligands. Several factors potentially affecting the ligand binding affinity and specificity were identified including net charges, dipole moments, and the presence of aromatic rings. On the basis of these findings, linear response models (LRMs) were built for different sets of ligands that showed high correlations (R(2) > 0.95) to the corresponding FEP binding affinities. These results identify some key factors that determine ligand-mGluR1 binding and could be used for future inhibitor designs and support a role for in silico modeling for understanding receptor ligand interactions.

Parametrisation of the free energy of ATP binding to wild-type and mutant Kir6.2 potassium channels

ATP-sensitive K(+) (K(ATP)) channels, comprised of pore-forming Kir6.x and regulatory SURx subunits, play important roles in many cellular functions; because of their sensitivity to inhibition by intracellular ATP, K(ATP) channels provide a link between cell metabolism and membrane electrical activity. We constructed structural homology models of Kir6.2 and a series of Kir6.2 channels carrying mutations within the putative ATP-binding site. Computational docking was carried out to determine the conformation of ATP in its binding site. The Linear Interaction Energy (LIE) method was used to estimate the free-energy of ATP binding to wild-type and mutant Kir6.2 channels. Comparisons of the theoretical binding free energies for ATP with those determined from mutational experiments enabled the identification of the most probable conformation of ATP bound to the Kir6.2 channel. A set of LIE parameters was defined that may enable prediction of the effects of additional Kir6.2 mutations within the ATP binding site on the affinity for ATP.

Molecular docking study and development of an empirical binding free energy model for phosphodiesterase 4 inhibitors

In the present work, several computational methodologies were combined to develop a model for the prediction of PDE4B inhibitors' activity. The adequacy of applying the ligand docking approach, keeping the enzyme rigid, to the study of a series of PDE4 inhibitors was confirmed by a previous molecular dynamics analysis of the complete enzyme. An exhaustive docking procedure was performed to identify the most probable binding modes of the ligands to the enzyme, including the active site metal ions and the surrounding structural water molecules. The enzyme-inhibitor interaction enthalpies, refined by using the semiempirical molecular orbital approach, were combined with calculated solvation free energies and entropy considerations in an empirical free energy model that enabled the calculation of binding free energies that correlated very well with experimentally derived binding free energies. Our results indicate that both the inclusion of the structural water molecules close to the ions in the binding site and the use of a free energy model with a quadratic dependency on the ligand free energy of solvation are important aspects to be considered for molecular docking investigations involving the PDE4 enzyme family.