Simulation‐Based Predictions of Binding Affinities of Matrix Metalloproteinase Inhibitors

Khandelwal, Akash; Lukáčová, Viera; Kroll, D. M.; Çömez, Doğan; Raha, Soumyendu; Baláž, Štefan

doi:10.1002/qsar.200430896

Cited by 8 publications

(13 citation statements)

References 77 publications

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“…Conformational sampling using 200-ps MD simulations did not improve the statistics significantly (r 2 = 0.589). We showed previously [18,32] that these correlations can be amended by a careful selection of the used intervals of MD simulations.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…Docking of the inhibitors to the MMP-9 structure taken from the Protein Data Bank (PDB file 1GKC) was performed using FlexX. , The ranking of poses was based upon the distance between catalytic zinc and both hydroxamate oxygens in the interval 1.5−2.5 Å as the primary criterion and the FlexX score as the secondary criterion . Docking provided a greater variety of the initial binding modes than superposition of the ligands with the ligand in the PDB file of the complex . Since the MD simulations routinely do not sample conformations too different from the starting conformations, this step was instrumental in selecting the binding mode explaining the experimental data for several compounds.…”

Section: Resultsmentioning

confidence: 99%

A Combination of Docking, QM/MM Methods, and MD Simulation for Binding Affinity Estimation of Metalloprotein Ligands

et al. 2005

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To alleviate the problems in the receptor-based design of metalloprotein ligands due to inadequacies in the force-field description of coordination bonds, a four-tier approach was devised. Representative ligand-metalloprotein interaction energies are obtained by subsequent application of (1) docking with metal-binding-guided selection of modes; (2) optimization of the ligand-metalloprotein complex geometry by combined quantum mechanics and molecular mechanics (QM/MM) methods; (3) conformational sampling of the complex with constrained metal bonds by force-field based molecular dynamics (MD); and (4) a single point QM/MM energy calculation for the time-averaged structures. The QM/MM interaction energies are, in a linear combination with the desolvation-characterizing changes in the solvent-accessible surface areas, correlated with experimental data. The approach was applied to structural correlation of published binding free energies of a diverse set of 28 hydroxamate inhibitors to zinc-dependent matrix metalloproteinase 9 (MMP-9). Inclusion of step 3 and step 4 significantly improved both correlation and prediction. The two descriptors explained 90% of variance in inhibition constants of all 28 inhibitors, ranging from 0.08 to 349 nM, with the average unassigned error of 0.318 log units. The structural and energetic information obtained from the timeaveraged MD simulation results helped understand the differences in binding modes of related compounds.

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“…In our previous study, 39 the MD-based LR correlations (Eq. 1) of the hydroxamate inhibitors 46 of MMP-9 behaved anomalously: the quality of correlations did not improve with increased simulation time and some outliers adopted comparatively different conformations during MD simulations.…”

Section: Resultsmentioning

confidence: 99%

“…The protocol was described in detail elsewhere. 39 The generated ensemble averages are summarized in the supporting information.…”

Section: Methodsmentioning

confidence: 99%

Processing Multimode Binding Situations in Simulation-Based Prediction of Ligand−Macromolecule Affinities

et al. 2005

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The Linear Response (LR) approximation and similar approaches belong to practical methods for estimation of ligand-receptor binding affinities. The approaches correlate experimental binding affinities with the changes upon binding of the ligand electrostatic and van der Waals energies, and of solvation characteristics. These attributes are expressed as ensemble averages that are obtained by conformational sampling of the protein-ligand complex and of the free ligand by molecular dynamics or Monte Carlo simulations. We observed that outliers in the LR correlations occasionally exhibit major conformational changes of the complex during sampling. We treated the situation as a multi-mode binding case, for which the observed association constant is the sum of the partial association constants of individual states/modes. The resulting nonlinear expression for the binding affinities contains all the LR variables for individual modes that are scaled by the same 2-4 adjustable parameters as in the one-mode LR equation. The multi-mode method was applied to inhibitors of a matrix metalloproteinase, where this treatment improved the explained variance in experimental activity from 75% for the uni-mode case to about 85%. The predictive ability scaled accordingly, as verified by extensive cross-validations.

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Simulation‐Based Predictions of Binding Affinities of Matrix Metalloproteinase Inhibitors

Cited by 8 publications

References 77 publications

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

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

A Combination of Docking, QM/MM Methods, and MD Simulation for Binding Affinity Estimation of Metalloprotein Ligands

Processing Multimode Binding Situations in Simulation-Based Prediction of Ligand−Macromolecule Affinities

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