Computation Of Binding Free Energy Research Articles

Fragment-Based Computation of Binding Free Energies by Systematic Sampling

A fragment-based method for computing protein-ligand binding free energies by systematic sampling has been developed. Systematic sampling of fragment-protein interactions in translational and rotational space is followed by de novo assembly of fragments into molecules and computation of binding free energies for the molecules with statistical mechanics. The rigorous sampling provides independence from the choice of initial binding pose and assembling fragments enables evaluation of binding of a large number of molecule poses with relatively little computation. The method allows a full sampling of possible conformations and avoids the "conformational focusing" problem associated with free energy methods that sample only limited conformational and orientation changes from a starting pose. The direct computation of the entropy loss upon assembling fragments into molecules is an innovation for fragment-based methods. The computed binding free energies are compared to calorimetric data for a series of ligands for the T4 lysozyme L99A mutant and binding constants for a series of p38 MAP kinase ligands. In both cases, the standard error of prediction is close to 1 kcal/mol.

Fourier transform convolution integrals applied to generalized Born molecular volume

Generalized Born methods are currently among the solvation models most commonly used for biological applications. We reformulate the generalized Born molecular volume method initially described by (Lee et al, 2003, J Phys Chem, 116, 10606; Lee et al, 2003, J Comp Chem, 24, 1348) using fast Fourier transform convolution integrals. Changes in the initial method are discussed and analyzed. Finally, the method is extensively checked with snapshots from common molecular modeling applications: binding free energy computations and docking. Biologically relevant test systems are chosen, including 855-36091 atoms. It is clearly demonstrated that, precision-wise, the proposed method performs as good as the original, and could better benefit from hardware accelerated boards.

Grand Canonical Free-Energy Calculations of Protein−Ligand Binding

The principles behind the computation of protein-ligand binding free energies by Monte Carlo simulation in the Grand Canonical Ensemble are described in detail, and two variations of the calculation are presented. The computation can be performed by bathing a protein binding site either with ligand images that interact with each other or with ligand images that pass through each other. The second method is theoretically more rigorous, but we show that both methods lead to the same result, and there are distinct numeric advantages to using ligand images that interact with each other. The Grand Canonical simulation provides gas-phase binding free energies that can be converted to aqueous energies by generalized Born-surface area (GB/SA) solvation calculations to provide values that agree with experiment within +/-1.5 kcal/mol. However, the accuracy of these simple solvation calculations is a major limiting factor in the accuracy of the overall binding free-energy computation. The Grand Canonical simulation has several characteristics beneficial to free-energy calculations. One is that the number of parameters that must be set for the simulation is small and can be determined objectively, making the outcome more deterministic, with respect to choice of input conditions, as compared to perturbation methods. Second, the simulation is free from assumptions about the starting pose or nature of the binding site. A final benefit is that binding free energies are a direct outcome of the simulation, and little processing is required to determine them.

Publisher’s Note: “Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations” [J. Chem. Phys. 128, 115103 (2008)

First Page

Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations

The binding of a ligand to a receptor is often associated with the displacement of a number of bound water molecules. When the binding site is exposed to the bulk region, this process may be sampled adequately by standard unbiased molecular dynamics trajectories. However, when the binding site is deeply buried and the exchange of water molecules with the bulk region may be difficult to sample, the convergence and accuracy in free energy perturbation (FEP) calculations can be severely compromised. These problems are further compounded when a reduced system including only the region surrounding the binding site is simulated. To address these issues, we couple molecular dynamics (MD) with grand canonical Monte Carlo (GCMC) simulations to allow the number of water to fluctuate during an alchemical FEP calculation. The atoms in a spherical inner region around the binding pocket are treated explicitly while the influence of the outer region is approximated using the generalized solvent boundary potential (GSBP). At each step during thermodynamic integration, the number of water in the inner region is equilibrated with GCMC and energy data generated with MD is collected. Free energy calculations on camphor binding to a deeply buried pocket in cytochrome P450cam, which causes about seven water molecules to be expelled, are used to test the method. It concluded that solvation free energy calculations with the GCMC/MD method can greatly improve the accuracy of the computed binding free energy compared to simulations with fixed number of water.

Conformational Change of HIV-1 Viral DNA after Binding with Integrase

The complex model structure of human immunodeficiency virus-1 (HIV-1) DNA with integrase (IN) dimer (IN2) was refined through molecular dynamics (MD) simulation, and the viral DNA conformational change was explored after binding with IN2. The result showed that the viral DNA could be divided into five regions (i.e. non-binding region, high-affinity region 1, weak-affinity region, high-affinity region 2, and reaction region) according to the binding power with IN2. The partition rationality for viral DNA was confirmed through binding free energy computation. Compared with the viral DNA before binding with IN2, some big conformational changes occurred for the bases in the four binding regions other than the non-binding region for the viral DNA complexed with IN2. The obvious deviation from standard B-DNA in the viral DNA main chain of the complex and the broading of the minor groove in the binding site were both the structural basis for the recognition of viral DNA with IN. The simulation results basically agreed with experimental data, which provided some structural information for the drug design based on the structure of HIV-1 IN.