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 (Cmax of 7.1 µM), JNJ-2482272 had plasma concentrations beneath the lower limit of quantification (3 ng/ml) after 6 consecutive days of dosing. To determine if JNJ-2482272 is an autoinducer in rats, plated rat hepatocytes were treated with JNJ-2482272 for 2 days. The major hydroxylated metabolites of JNJ-2482272 were isolated and characterized by mass spectrometry and NMR analyses. Compared with the vehicle-treated cells, a concentration-dependent increase was observed in the formation of phase I- and II-mediated metabolites coinciding with greater expression of cytochrome P450s (P450s) and UDP-glucuronosyltransferases (UGTs) in rat hepatocytes. CYP1A1, CYP1A2, CYP1B1, and UGT1A6 transcripts were predominantly induced, suggesting that JNJ-2482272 is an activator of the aryl hydrocarbon receptor (AhR). In a human AhR reporter assay, JNJ-2482272 demonstrated potent AhR activation with an EC50 value of 0.768 nM, a potency more comparable to the strong AhR activator and toxin 2,3,7,8-tetrachloro-dibenzodioxin than to weaker AhR activators 3-methylcholanthrene, ß-naphthoflavone, and omeprazole. In plated human hepatocytes, JNJ-2482272 induced CYP1A1 gene expression with an EC50 of 20.4 nM and increased CYP1A activity >50-fold from basal levels. In human recombinant P450s, JNJ-2482272 was exclusively metabolized by the CYP1 family of enzymes and most rapidly by CYP1A1. The summation of these in vitro findings bridges the in vivo conclusion that JNJ-2482272 is a strong autoinducer in rats and potentially in humans through potent AhR activation. SIGNIFICANCE STATEMENT: Drugs that induce their own metabolism (autoinducers) can lack sustained exposures for pharmacology and safety assessment hindering their development. JNJ-2482272 is demonstrated herein as a strong aryl hydrocarbon receptor (AhR) activator and CYP1A autoinducer, explaining its near complete loss of exposure after repeat administration in rat, which is likely translatable to human (if progressed further) considering its nanomolar potency comparable to "classical" AhR ligands like 2,3,7,8-tetrachloro-dibenzo-dioxin despite bearing a "nonclassical" drug structure.

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. Here, we apply a hybrid method that combines nonequilibrium candidate Monte Carlo (NCMC) simulations and molecular dynamics (MD) to enhance sampling of water in specific areas of a system, such as the binding site of a protein. Our approach uses NCMC to gradually remove interactions between a selected water molecule and its environment, then translates the water to a new region, before turning the interactions back on. This approach of gradual removal of interactions, followed by a move and then reintroduction of interactions, allows the environment to relax in response to the proposed water translation, improving acceptance of moves and thereby accelerating water exchange and sampling. We validate this approach on several test systems including the ligand-bound MUP-1 and HSP90 proteins with buried crystallographic waters removed. We show that our BLUES (NCMC/MD) method enhances water sampling relative to normal MD when applied to these systems. Thus, this approach provides a strategy to improve water sampling in molecular simulations which may be useful in practical applications in drug discovery and biomolecular 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. Here, we computed relative binding free energies of a prototypical Hv1 blocker on a model of human Hv1 in an open state. We used alchemical free energy perturbation techniques based on atomistic molecular dynamics simulations. The results support our proposed open state model and shed light on the preferred tautomeric state of the channel blocker. This work lays the groundwork for future studies on adapting the blocker molecule for more effective inhibition of 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. We evaluate each method's effectiveness in identifying the dominant binding mode and finding additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures. We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 µs each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions between binding modes in our study. Following this, we build Markov State Models to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD, we have better success in sampling the crystal structure while obtaining the correct populations.

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. The NCMC move proposal is divided into three parts. The flexible part of the ligand is alchemically turned off by decreasing the electrostatics and steric interactions gradually, followed by rotating the rotatable bond by a random angle and then slowly turning the ligand back on to its fully interacting state. The alchemical steps prior to and after the move proposal help the surrounding protein and water atoms in the binding pocket relax around the proposed ligand conformation and increase move acceptance rates. The protein-ligand system is propagated using classical MD in between the NCMC proposals. Using this MD/NCMC method, we were able to correctly reproduce the different binding modes of inhibitors binding to two kinase targets-c-Jun N-terminal kinase-1 and cyclin-dependent kinase 2-at a much lower computational cost compared to conventional MD and umbrella sampling. This method is available as a part of the BLUES software package.

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. We disprove this assumption and identify and characterize the enzymatic pathways responsible for high background incorporation. We demonstrate that mammalian cells can incorporate uracil analogs and characterize the enzymatic pathways responsible for high background incorporation. To overcome these limitations, we developed a new small molecule-enzyme pair consisting of uridine/cytidine kinase 2 and 2'-azidouridine. We demonstrate that 2'-azidouridine is only incorporated in cells expressing uridine/cytidine kinase 2 and characterize selectivity mechanisms using molecular dynamics and X-ray crystallography. Furthermore, this pair can be used to purify and track RNA from specific cellular populations, making it ideal for high-resolution cell-specific RNA labeling. Overall, these results reveal new aspects of mammalian salvage pathways and serve as a new benchmark for designing, characterizing and evaluating methodologies for cell-specific labeling of biomolecules.

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. We used BLUES to investigate binding modes of caffeine in the active site of its metabolizing enzyme Cytochrome P450 1A2 with the aim of elucidating metabolite-formation profiles at different concentrations. Because the activation energies of all sites of metabolism do not show a clear preference for one metabolite over the others, the orientations in the active site must play a key role. In simulations with caffeine located in a spacious pocket above the I-helix, it points N3 and N1 to the heme iron, whereas in simulations where caffeine is in close proximity to the heme N7 and C8 are preferably oriented toward the heme iron. We propose a mechanism where at low caffeine concentrations caffeine binds to the upper part of the active site, leading to formation of the main metabolite paraxanthine. On the other hand, at high concentrations two molecules are located in the active site, forcing one molecule into close proximity to the heme and yielding metabolites theophylline and trimethyluretic acid. Our results offer an explanation of previously published experimental results.

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. The time scale of side chain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed nonequilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of side chain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target side chain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine side chain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances side chain sampling, especially in systems where the torsional barrier to rotation is high (≥10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment. Overall, this may provide a promising strategy to selectively improve side chain sampling in 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. This approach, which we call indirect chemical perception because it operates on the intermediate graph of atom-typed nodes, creates a number of technical problems. For example, atom types must be sufficiently complex to encode all necessary information about the molecular environment, making it difficult to extend force fields encoded this way. Atom typing also results in a proliferation of redundant parameters applied to chemically equivalent classes of valence terms, needlessly increasing force field complexity. Here, we describe a new approach to assigning force field parameters via direct chemical perception. Rather than working through the intermediary of the atom-typed graph, direct chemical perception operates directly on the unmodified chemical graph of the molecule to assign parameters. In particular, parameters are assigned to each type of force field term (e.g., bond stretch, angle bend, torsion, and Lennard-Jones) based on standard chemical substructure queries implemented via the industry-standard SMARTS chemical perception language, using SMIRKS extensions that permit labeling of specific atoms within a chemical pattern. We use this to implement a new force field format, called the SMIRKS Native Open Force Field (SMIRNOFF) format. We demonstrate the power and generality of this approach using examples of specific molecules that pose problems for indirect chemical perception and construct and validate a minimalist yet very general force field, SMIRNOFF99Frosst. We find that a parameter definition file only ∼300 lines long provides coverage of all but <0.02% of a 5 million molecule drug-like test set. Despite its simplicity, the accuracy of SMIRNOFF99Frosst for small molecule hydration free energies and selected properties of pure organic liquids is similar to that of the General Amber Force Field, whose specification requires thousands of parameters. This force field provides a starting point for further optimization and refitting work to follow.

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. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.

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. We apply this approach to characterize membrane-mediated action of the diphtheria toxin translocation domain. First, we use a combination of the steady-state and time-resolved fluorescence spectroscopy to characterize bilayer penetration of the NBD probe selectively attached to different sites of the protein into membranes containing lipid-attached nitroxyl quenching groups. The constructed quenching profiles are analyzed with the Distribution Analysis methodology allowing for accurate determination of transverse distribution of the probe. The results obtained for 12 NBD-labeled single-Cys mutants are consistent with the so-called Open-Channel topology model. The experimentally determined quenching profiles for labeling sites corresponding to L350, N373, and P378 were used as initial constraints for positioning TH8-9 hairpin into the lipid bilayer for Molecular Dynamics simulation. Finally, we used alchemical free energy calculations to characterize protonation of E362 in soluble translocation domain and membrane-inserted conformation of its TH8-9 fragment. Our results indicate that membrane partitioning of the neutral E362 is more favorable energetically (by ~ 6 kcal/mol), but causes stronger perturbation of the bilayer, than the charged E362.

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. In this study, we model recent experimental results that show the progressive opening of the binding pocket in response to a series of homologous ligands.1 Even while using enhanced sampling techniques, we demonstrate that the predicted relative binding free energies (RBFE) are sensitive to the initial protein conformational state. Particularly, we highlight the importance of sufficient sampling of protein conformational changes and demonstrate how inclusion of three key protein residues in the "hot" region of the FEP/REST simulation improves the sampling and resolves this sensitivity, given enough simulation time.

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 1.2 kcal/mol range for several of the best performing methods. Top-performers included a quantum mechanical approach with continuum solvent models and functional group corrections, alchemical molecular dynamics simulations with a classical all-atom force field, and a single-conformation Poisson-Boltzmann approach. While 1.2 kcal/mol is still a significant error, experimental hydration free energies covered a range of nearly 20 kcal/mol, so methods typically showed substantial predictive power. Here, a substantial new focus was on evaluation of error estimates, as predicting when a computational prediction is reliable versus unreliable has considerable practical value. We found, however, that in many cases errors are substantially underestimated, and that typically little effort has been invested in estimating likely error. We believe this is an important area for further research.

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. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.

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. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger's and OpenEye's APIs, has been made available freely under the BSD license.