Linear interaction energy approximation for binding affinities of nevirapine and HEPT analogues with HIV-1 reverse transcriptase

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Six novel NNRTIs (DABO) with high efficacy are designed by assessing the interaction potential and structural requirements using chemometric analyses (SVM, BPNN and MLR) on structural descriptors.

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.