49 research outputs found
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 MT<sub>2</sub> 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 MT<sub>2</sub> 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 MT<sub>2</sub> 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 MT<sub>2</sub> 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
MT<sub>2</sub> 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 MT<sub>2</sub> 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). H<sub>1</sub> is nearly equidistant between N and N<sub>1</sub> 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, H<sub>1</sub> 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