Characterization of JNJ-2482272 [4-(4-Methyl-2-(4-(Trifluoromethyl)Phenyl)Thiazole-5-yl) Pyrimidine-2-Amine] As a Strong Aryl Hydrocarbon Receptor Activator in Rat and Human.

[4-(4-Methyl-2-(4-(trifluoromethyl)phenyl)thiazole-5-yl)pyrimidine-2-amine] (JNJ-2482272), under investigation as an anti-inflammatory agent, was orally administered to rats once daily at 60 mg/kg for 6 consecutive days. Despite high plasma exposure after single administration (C of 7.1 M), JNJ-2482272 had plasma concentrations beneath the lower limit of quantification (3 ng/ml) after 6 consecutive days of dosing.

Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo.

Water molecules can be found interacting with the surface and within cavities in proteins. However, water exchange between bulk and buried hydration sites can be slow compared to simulation timescales, thus leading to the inefficient sampling of the locations of water. This can pose problems for free energy calculations for computer-aided drug design.

Insights on small molecule binding to the Hv1 proton channel from free energy calculations with molecular dynamics simulations.

Hv1 is a voltage-gated proton channel whose main function is to facilitate extrusion of protons from the cell. The development of effective channel blockers for Hv1 can lead to new therapeutics for the treatment of maladies related to Hv1 dysfunction. Although the mechanism of proton permeation in Hv1 remains to be elucidated, a series of small molecules have been discovered to inhibit Hv1.

Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations.

Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC)-based method (NCMC + MD) perform in predicting binding modes for a set of 12 fragment-like molecules, which bind to soluble epoxide hydrolase.

Sampling Conformational Changes of Bound Ligands Using Nonequilibrium Candidate Monte Carlo and Molecular Dynamics.

Flexible ligands often have multiple binding modes or bound conformations that differ by rotation of a portion of the molecule around internal rotatable bonds. Knowledge of these binding modes is important for understanding the interactions stabilizing the ligand in the binding pocket, and other studies indicate it is important for calculating accurate binding affinities. In this work, we use a hybrid molecular dynamics (MD)/nonequilibrium candidate Monte Carlo (NCMC) method to sample the different binding modes of several flexible ligands and also to estimate the population distribution of the modes.

An optimized chemical-genetic method for cell-specific metabolic labeling of RNA.

Tissues and organs are composed of diverse cell types, which poses a major challenge for cell-type-specific profiling of gene expression. Current metabolic labeling methods rely on exogenous pyrimidine analogs that are only incorporated into RNA in cells expressing an exogenous enzyme. This approach assumes that off-target cells cannot incorporate these analogs.

Binding Modes and Metabolism of Caffeine.

A correct estimate of ligand binding modes and a ratio of their occupancies is crucial for calculations of binding free energies. The newly developed method BLUES combines molecular dynamics with nonequilibrium candidate Monte Carlo. Nonequilibrium candidate Monte Carlo generates a plethora of possible binding modes and molecular dynamics enables the system to relax.

Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo.

Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation time scales, yet critical. Specifically, adequate sampling of side chain motions in protein binding pockets is crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations.

Escaping Atom Types in Force Fields Using Direct Chemical Perception.

Traditional approaches to specifying a molecular mechanics force field encode all the information needed to assign force field parameters to a given molecule into a discrete set of atom types. This is equivalent to a representation consisting of a molecular graph comprising a set of vertices, which represent atoms labeled by atom type, and unlabeled edges, which represent chemical bonds. Bond stretch, angle bend, and dihedral parameters are then assigned by looking up bonded pairs, triplets, and quartets of atom types in parameter tables to assign valence terms and using the atom types themselves to assign nonbonded parameters.

Refining Protein Penetration into the Lipid Bilayer Using Fluorescence Quenching and Molecular Dynamics Simulations: The Case of Diphtheria Toxin Translocation Domain.

Dynamic disorder of the lipid bilayer presents a challenge for establishing structure-function relationships in membranous systems. The resulting structural heterogeneity is especially evident for peripheral and spontaneously inserting membrane proteins, which are not constrained by the well-defined transmembrane topology and exert their action in the context of intimate interaction with lipids. Here, we propose a concerted approach combining depth-dependent fluorescence quenching with Molecular Dynamics simulation to decipher dynamic interactions of membrane proteins with the lipid bilayers.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo.

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant.

Sensitivity in Binding Free Energies Due to Protein Reorganization.

Tremendous recent improvements in computer hardware, coupled with advances in sampling techniques and force fields, are now allowing protein-ligand binding free energy calculations to be routinely used to aid pharmaceutical drug discovery projects. However, despite these recent innovations, there are still needs for further improvement in sampling algorithms to more adequately sample protein motion relevant to protein-ligand binding. Here, we report our work identifying and studying such clear and remaining needs in the apolar cavity of T4 lysozyme L99A.

Blind prediction of solvation free energies from the SAMPL4 challenge.

Here, we give an overview of the small molecule hydration portion of the SAMPL4 challenge, which focused on predicting hydration free energies for a series of 47 small molecules. These gas-to-water transfer free energies have in the past proven a valuable test of a variety of computational methods and force fields. Here, in contrast to some previous SAMPL challenges, we find a relatively wide range of methods perform quite well on this test set, with RMS errors in the 1.

Blind prediction of HIV integrase binding from the SAMPL4 challenge.

Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components--a small "virtual screening" challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges.

Lead optimization mapper: automating free energy calculations for lead optimization.

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds.