Improved estimation of ligand-macromolecule binding affinities by linear response approach using a combination of multi-mode MD simulation and QM/MM methods.
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 Aquist, 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.
Khandelwal
Akash
A
Department of Pharmaceutical Sciences, College of Pharmacy, North Dakota State University, Sudro Hall Suite 8, Fargo, ND 58105, USA.
Balaz
Stefan
S
D003198:Computer Simulation; D008024:Ligands; D020780:Matrix Metalloproteinase 9 / Q000737:chemistry / Q000378:metabolism; D061965:Matrix Metalloproteinase Inhibitors; D008956:Models, Chemical; D011485:Protein Binding
D008024:Ligands; D061965:Matrix Metalloproteinase Inhibitors; D020780:Matrix Metalloproteinase 9