A Molecular Basis for the Selectivity of Thiadiazole Urea Inhibitors with Stromelysin-1 and Gelatinase-A from Generalized Born Molecular Dynamics Simulations

Rizzo, Robert C.; Toba, Samuel; Kuntz, Irwin D.

doi:10.1021/jm030570k

Cited by 72 publications

(71 citation statements)

References 50 publications

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“…Several variations of MM/GBSA models have been tested in recent years and compared with experimental data and microscopic models [75][76][77][78][79][80]. For the majority of applications, MM/GBSA models showed the strong correlation between experimental and computational data.…”

Section: Macroscopic Models Turn Microscopicmentioning

confidence: 99%

Electrostatics in Proteins and Protein–Ligand Complexes

Kukić

Nielsen

2010

Future Med. Chem.

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Section: Macroscopic Models Turn Microscopicmentioning

confidence: 99%

Electrostatics in Proteins and Protein–Ligand Complexes

Kukić

Nielsen

2010

Future Med. Chem.

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“…The distances of coordinating ligand atoms from zinc are reasonable: hydroxamate oxygens oscillate around 1.85 ä and 1.90 ä, histidine nitrogens around 1.95 ä [35]. Carefully constructed initial geometries and identical zinc binding groups in all ligands may have contributed to simulation of the correct coordination sphere.…”

Section: Analysis Of MD Simulation Datamentioning

confidence: 99%

“…In the first stream, representative structures are generated using molecular mechanics (MM)-based molecular dynamics (MD) simulations with explicit water [33,34] or with the Generalized Born (GB) continuum model [35]. Free energy of binding is given as the sum of the differences D for (1) the force-field energy calculated after removal of solvent and counter-ions; (2) the polar solvation energy, calculated as a numerical solution of the Poisson-Boltzmann (PB) equation [33,34] or with GB model [33,35]; (3) an empirical estimate of the nonpolar solvation energy based on the solvent-accessible surface area (SA ± this acronym only used in this paragraph); and (4) entropy estimated using normal-mode analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Free energy of binding is given as the sum of the differences D for (1) the force-field energy calculated after removal of solvent and counter-ions; (2) the polar solvation energy, calculated as a numerical solution of the Poisson-Boltzmann (PB) equation [33,34] or with GB model [33,35]; (3) an empirical estimate of the nonpolar solvation energy based on the solvent-accessible surface area (SA ± this acronym only used in this paragraph); and (4) entropy estimated using normal-mode analysis. The resulting approaches (MM-PBSA [33,34], MM-GBSA [35]) do not require parameter optimization, at least in the final stage.…”

Section: Introductionmentioning

confidence: 99%

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Simulation‐Based Predictions of Binding Affinities of Matrix Metalloproteinase Inhibitors

Khandelwal¹,

Lukáčová²,

Kroll³

et al. 2004

QSAR Comb. Sci.

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Inhibitory potencies of 28 hydroxamate derivatives to matrix metalloproteinase 9 (MMP‐9) have been modeled using the Linear Response method that describes free energy of binding as a linear combination of ensemble averages of van der Waals, electrostatic, and cavity‐related terms. Individual terms are differences in the respective quantities between the bound and free ligands, which were determined using two methods: a hybrid Monte Carlo method within the frame of Surface‐generalized Born Continuum Solvation Model and Molecular Dynamics simulation. The MD simulations of ligands in the hydrated MMP‐9 active site, as well as of free ligands in water were carried out for 200 ps. The time‐averaged structures of the ligands bound with the protein and of the free ligands were collected at 5, 10, 25, 50, 100, and 200 ps. The best correlations between experimental and calculated binding energies, obtained using the training set containing 21 structurally diverse compounds, explained about 90% of experimental variance. The predicted binding affinities for the test set of 7 inhibitors were in good agreement with the experimental data (RMSE=0.944). Similar RMSE values were observed with 200 random selections of the 7‐member test set. The loss of polar and nonpolar solvent accessible surface areas upon binding was identified as the main phenomenon contributing to affinity, accompanied by the enhancement of van der Waals interactions upon binding.

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“…The methods usually use conformational sampling of the ligand-receptor complex and of free reactants by Molecular Dynamics (MD) or Monte Carlo (MC) simulations, although the use of multiple structures corresponding to energy minima [5] or single optimized structures to speed up the calculations have also been advocated [6,7]. Other differences appear in the following areas: the treatment of solvent (explicit [8], continuum [6,9], or in vacuo [6,10]), estimation of the electrostatic component of solvation energies (linearized Poisson-Boltzman equation [5,6,[10][11][12][13], the generalized Born model [9,12,14], or the pair-wise Coulomb relations in explicit solvent [15]), and the parameter optimization (used [16] or not used [5,13]). …”

Section: Introductionmentioning

confidence: 99%

Improved estimation of ligand–macromolecule binding affinities by linear response approach using a combination of multi-mode MD simulation and QM/MM methods

Khandelwal

Baláž

2007

J Comput Aided Mol Des

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Structure-based predictions of binding affinities of ligands binding to proteins by coordination bonds with transition metals, covalent bonds, and bonds involving charge re-distributions are hindered by the absence of proper force fields. This shortcoming affects all methods which use force-field-based molecular simulation data on complex formation for affinity predictions. One of the most frequently used methods in this category is the Linear Response (LR) approach of Åquist, correlating binding affinities with van der Waals and electrostatic energies, as extended by Jorgensen's inclusion of solvent-accessible surface areas. All these terms represent the differences, upon binding, in the ensemble averages of pertinent quantities, obtained from molecular dynamics (MD) or Monte Carlo simulations of the complex and of single components. Here we report a modification of the LR approach by: (1) the replacement of the two energy terms through the single-point QM/MM energy of the time-averaged complex structure from an MD simulation; and (2) a rigorous consideration of multiple modes (mm) of binding. The first extension alleviates the force-field related problems, while the second extension deals with the ligands exhibiting large-scale motions in the course of an MD simulation. The second modification results in the correlation equation that is nonlinear in optimized coefficients, but does not lead to an increase in the number of optimized coefficients. The application of the resulting mm QM/MM LR approach to the inhibition of zinc-dependent gelatinase B (matrix metalloproteinase 9) by 28 hydroxamate ligands indicates a significant improvement of descriptive and predictive abilities.

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A Molecular Basis for the Selectivity of Thiadiazole Urea Inhibitors with Stromelysin-1 and Gelatinase-A from Generalized Born Molecular Dynamics Simulations

Cited by 72 publications

References 50 publications

Electrostatics in Proteins and Protein–Ligand Complexes

Electrostatics in Proteins and Protein–Ligand Complexes

Simulation‐Based Predictions of Binding Affinities of Matrix Metalloproteinase Inhibitors

Improved estimation of ligand–macromolecule binding affinities by linear response approach using a combination of multi-mode MD simulation and QM/MM methods

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