Calculation of Protein-Ligand Binding Affinities

Gilson, Michael K.; Zhou, Huan‐Xiang

doi:10.1146/annurev.biophys.36.040306.132550

Cited by 834 publications

(945 citation statements)

References 136 publications

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“…Hence, our reference will not be the experimental data, but the simulations using full protein. Therefore, we made the simulated protein-ligand complexes systematically smaller by simulating systems with radii of 40,35,30,25,20, or 15 Å (the full system has a radius of 46 Å). The results of these simulations are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Improving the Efficiency of Protein–Ligand Binding Free-Energy Calculations by System Truncation

Genheden

Ryde

2012

J. Chem. Theory Comput.

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We have studied whether the efficiency of alchemical free-energy calculations with the Bennett acceptance ratio method of protein-ligand binding energies can be improved by simulating only a part of the protein. To this end, we solvated the full protein in a spherical droplet with a radius of 46 Å, surrounded by vacuum. Then, we systematically reduced the size of the droplet and at the same time ignored protein residues that were outside the droplet. Radii of 40 to 15 Å were tested. Ten inhibitors of the blood clotting factor Xa were studied and the results were compared to an earlier study in which the protein was solvated in a periodic box, showing complete agreement between the two set of calculations within statistical uncertainty. We then show that the simulated system can be truncated down to 15 Å, without changing the calculated affinities by more than 0.5 kJ/mol on average (maximum difference 1.4 kJ/mol). Moreover, we show that reducing the number of intermediate states in the calculations from eleven to three gave deviations that on average were only 0.5 kJ/mol (maximum 1.4 kJ/mol). Together this shows that truncation is an appropriate way to improve efficiency of free-energy calculations for small mutations that preserve the net charge of the ligand. In fact, each calculation of a relative binding affinity requires only 6 simulations, each of which takes ~15 CPU hours of computation on a single processor.Keywords: free-energy perturbation, Bennett acceptance ratio, ligand-binding affinities, periodic boundary conditions, system truncation, long-range electrostatics.2 Introduction Accurate estimation of protein-ligand binding affinities is a major challenge in computational chemistry. Although formally correct relative free energies can be obtained by alchemical free-energy techniques such a free energy perturbation (FEP) and thermodynamic integration (TI), they have found little use outside academia. 1,2 The main reason for this is that such methods are computationally demanding, because they require simulations of unphysical intermediate states involving extensive sampling of the phase space. 3 More approximate methods to estimate binding affinities exists, which do not require simulations of intermediate states. 4 They are usually referred to as end-points methods because they sample only the complex, and possibly the free protein and the free ligand. 5 A popular method in this class is MM/GBSA (molecular mechanics with generalised Born and surface-area solvation). 6,7 Although it only requires a simulation of the complex, we have shown that it can actually be computationally more expensive than TI because it is intrinsically imprecise and requires averaging over many independent simulations to reach a precision comparable to that of FEP or TI. 8 In addition, the accuracy of some of the terms in MM/GBSA have been questioned and the method often fails to give a useful accuracy of the predicted affinities. 9,10,11 Another popular end-point method is the linear interaction energy (LIE). 12 We ...

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Section: Resultsmentioning

confidence: 99%

Improving the Efficiency of Protein–Ligand Binding Free-Energy Calculations by System Truncation

Genheden

Ryde

2012

J. Chem. Theory Comput.

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“…This change in hydration is a critical component of binding affinities in aqueous solution; [1][2][3] hence, simulation methods that predict absolute binding free energies require accurate hydration models. Implementation of accurate hydration models in scoring functions would benefit promising applications such as virtual screening in drug discovery.…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

Sulea

Corbeil

Purisima

2010

J. Chem. Theory Comput.

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Abstract:The present study provides a comprehensive systematic analysis on the applicability of the linear interaction energy (LIE) approximation to the prediction of gas-to-water transfer (hydration) free energy. The study is based on molecular dynamics simulations in explicit solvent for an extensive and diverse hydration data set comprising 564 neutral compounds with measured hydration free energies, including a "traditional" data set and the more challenging drug-like SAMPL1 data set. A highly correlative LIE model was achieved without empirical scaling of the solute-solvent interaction energy terms along with a cavity term calibrated to the experiment. This model was particularly accurate for the "traditional" data set and of acceptable accuracy for the SAMPL1 data set, with mean-unsigned-errors below 1 kcal/mol and slightly above 2 kcal/mol, respectively. We have analyzed the sensitivity of the LIE model to several parameters such as continuum correction terms applied outside the explicit water shell, the impact of various charging methods, the applicability of single-conformer representation of the solute, and the inclusion of internal energy terms. The parameters with the greatest sensitivity are the charging methods used, with AM1BCC-SP (without AM1 geometry optimization) charges favored over AM1BCC-OPT and RESP charges. The inclusion of the change in intramolecular van der Waals and electrostatic energies between the solution and gas phases can also lead to improved prediction accuracies. Functional group based error analysis identified several chemical classes as minor outliers with systematic errors. A direct comparison of the LIE and free energy perturbation (FEP) approaches using the same force field and charging method shows that the LIE approximation is at least as accurate as the FEP approach with a reduction of computing time by at least 1 order of magnitude.

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“…As a computationally efficient explicitsolvent method, CGH-MD is potentially useful for simulating systems of large number of water particles to complement more rigorous methods. It may also be applied to the study of polar and hydrophobic effects, 13 nonuniformly distributed electrostatic interactions, and the effects associated with bound and sequestered water molecules 12,13 in various bio-macromolecular and nanofluidic systems such as the electrophoresis of DNA, 36 proteins, 60 viral particles, and complexes 61 in nanofluidic, 34,58,59,62 microfluidic, 63,64 and microstructure array 16,65 systems. …”

Section: Ion Distribution Patternmentioning

confidence: 99%

“…6,7 These simulation studies have yielded good agreement with observations. [8][9][10][11] However, some of the explicit solvent effects such as nonuniformly distributed electrostatic interactions, hydrophobic effects, and bound and sequestered water molecules 12,13 cannot be fully considered without using explicit solvent methods. As these effects play important roles in electrophoresis and other processes, it is highly desired to explore explicit solvent methods in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Simulation of DNA electrophoresis in systems of large number of solvent particles by coarse‐grained hybrid molecular dynamics approach

Wang

Liu

et al. 2008

J Comput Chem

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Simulation of DNA electrophoresis facilitates the design of DNA separation devices. Various methods have been explored for simulating DNA electrophoresis and other processes using implicit and explicit solvent models. Explicit solvent models are highly desired but their applications may be limited by high computing cost in simulating large number of solvent particles. In this work, a coarse-grained hybrid molecular dynamics (CGH-MD) approach was introduced for simulating DNA electrophoresis in explicit solvent of large number of solvent particles. CGH-MD was tested in the simulation of a polymer solution and computation of nonuniform charge distribution in a cylindrical nanotube, which shows good agreement with observations and those of more rigorous computational methods at a significantly lower computing cost than other explicit-solvent methods. CGH-MD was further applied to the simulation of DNA electrophoresis in a polymer solution and in a well-studied nanofluidic device. Simulation results are consistent with observations and reported simulation results, suggesting that CGH-MD is potentially useful for studying electrophoresis of macromolecules and assemblies in nanofluidic, microfluidic, and microstructure array systems that involve extremely large number of solvent particles, nonuniformly distributed electrostatic interactions, bound and sequestered water molecules.

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Calculation of Protein-Ligand Binding Affinities

Cited by 834 publications

References 136 publications

Improving the Efficiency of Protein–Ligand Binding Free-Energy Calculations by System Truncation

Improving the Efficiency of Protein–Ligand Binding Free-Energy Calculations by System Truncation

Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

Simulation of DNA electrophoresis in systems of large number of solvent particles by coarse‐grained hybrid molecular dynamics approach

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