Quantum Mechanics/Molecular Mechanics Modeling of Covalent Addition between EGFR–Cysteine 797 and <i>N</i>‑(4-Anilinoquinazolin-6-yl) Acrylamide

Irreversible epidermal growth factor receptor (EGFR) inhibitors can circumvent resistance to first-generation ATP-competitive inhibitors in the treatment of nonsmall-cell lung cancer. They covalently bind a noncatalytic cysteine (Cys797) at the surface of EGFR active site by an acrylamide warhead. Herein, we used a hybrid quantum mechanics/molecular mechanics (QM/MM) potential in combination with umbrella sampling in the path-collective variable space to investigate the mechanism of alkylation of Cys797 by the prototypical covalent inhibitor <i>N</i>-(4-anilinoquinazolin-6-yl) acrylamide. Calculations show that Cys797 reacts with the acrylamide group of the inhibitor through a direct addition mechanism, with Asp800 acting as a general base/general acid in distinct steps of the reaction. The obtained reaction free energy is negative (Δ<i>A</i> = −12 kcal/mol) consistent with the spontaneous and irreversible alkylation of Cys797 by <i>N</i>-(4-anilinoquinazolin-6-yl) acrylamide. Our calculations identify desolvation of Cys797 thiolate anion as a key step of the alkylation process, indicating that changes in the intrinsic reactivity of the acrylamide would have only a minor impact on the inhibitor potency

Structure-Based Virtual Screening of MT₂ Melatonin Receptor: Influence of Template Choice and Structural Refinement

Developing GPCR homology models for structure-based virtual screening requires the choice of a suitable template and refinement of binding site residues. We explored this systematically for the MT₂ melatonin receptor, with the aim to build a receptor homology model that is optimized for the enrichment of active melatoninergic ligands. A set of 12 MT₂ melatonin receptor models was built using different GPCR X-ray structural templates and submitted to a virtual screening campaign on a set of compounds composed of 29 known melatonin receptor ligands and 2560 drug-like decoys. To evaluate the effect of including a priori information in receptor models, 12 representative melatonin receptor ligands were placed into the MT₂ receptor models in poses consistent with known mutagenesis data and with assessed pharmacophore models. The receptor structures were then adapted to the ligands by induced-fit docking. Most of the 144 ligand-adapted MT₂ receptor models showed significant improvements in screening enrichments compared to the unrefined homology models, with some template/refinement combinations giving excellent enrichment factors. The discriminating ability of the models was further tested on the 29 active ligands plus a set of 21 inactive or low-affinity compounds from the same chemical classes. Rotameric states of side chains for some residues, presumed to be involved in the binding process, were correlated with screening effectiveness, suggesting the existence of specific receptor conformations able to recognize active compounds. The top MT₂ receptor model was able to identify 24 of 29 active ligands among the first 2% of the screened database. This work provides insights into the use of refined GPCR homology models for virtual screening

Free energy surface (FES) for TAU hydrolysis catalyzed by CBAH.

<p>Left panel. Bi-dimensional FES in the <i>S</i> and <i>Z</i> space from US simulations. The minimum free energy path is displayed with a continuous black line. Configurations <b>1</b>–<b>5</b> are crucial structures identified along the reaction path, <i>S</i>. Right panel, subpanel (A). Projection of the FES of TAU hydrolysis on <i>S</i>. Right panel, subpanel B. Relevant interatomic distances (reported as average over US with error bars representing the standard deviations) are plotted as function of <i>S</i>. Right panel, subpanel C. Improper torsion of the amide nitrogen (N) of TAU as function of <i>S</i>. Right panel, subpanel D. Nucleophile attacking angle as function of <i>S</i>.</p

Activation barriers for TAU hydrolysis as obtained from steered-MD/PCVs simulations.

<p>Barriers are estimated from work profiles and are refereed to CBAH wild type (wt) and to zero-point charge mutants.</p

Catalytic mechanism of Ntn-hydrolases.

<p>Panel (A). The reaction begins when the nucleophilic oxygen/sulfur of Thr/Ser/Cys donates its proton to its own alpha-amino group and attacks the carbonyl carbon of the substrate <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032397#pone.0032397-Oinonen1" target="_blank">[1]</a>, leading to a negatively charged tetrahedral intermediate (X represents oxygen or sulfur). The acylation step is completed when the alpha-amino group of the catalytic residue protonates the nitrogen of the scissile amide bond leading to the expulsion of the leaving group. Panel (B) First reaction of the catalytic mechanism of CBAH. <b>A</b>, <b>B</b>, and <b>C</b> are key steps for the cleavage of TAU amide bond.</p

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA‑D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure–kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed

Transition state (TS) and tetrahedral adduct (TA) geometries identified along the path.

<p>Left panel (A). TS structure of TAU (gray carbons) hydrolysis catalyzed by CBAH (black carbons). H₁ is nearly equidistant between N and N₁ favoring the formation of a pseudo chair structure. Right panel (B) Zwitterionic TI. In both panels, H-bonds are shown as dotted green lines, while secondary structure elements of CBAH are omitted for clarity.</p

Free energy profile for Cys2 internal proton transfer.

<p>Free energy profile along S, H₁ distance as obtained from umbrella sampling calculations and WHAM.</p

CBAH residues interacting with Cys2 as found in the crystal structure.

<p>Secondary structure elements of CBAH are displayed with gray cartoons, while carbon atoms are in black.</p

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA‑D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure–kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed