Molecular Dynamics Simulation-Based Evaluation of the Binding Free Energies of Computationally Designed Drug Candidates: Importance of the Dynamical Effects

Yamashita, Takefumi; Ueda, Akihiko; Mitsui, Takashi; Tomonaga, Atsushi; Matsumoto, Shunji; Kodama, Tatsuhiko; Fujitani, Hideaki

doi:10.1248/cpb.c14-00132

Cited by 13 publications

(12 citation statements)

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“…This result is essentially the same as in ref. 12. We, therefore, consider that the dynamical effects are necessary to quantitatively predict the binding free energy and that MD-based binding free energy prediction methods will play a key role in making the drug discovery procedure faster and more efficient.…”

Section: Resultsmentioning

confidence: 99%

“…To demonstrate the feasibility of the binding free energy prediction procedure, we adopted the same target protein as that in ref. 12 ( Fig. 2).…”

Section: Methodsmentioning

confidence: 97%

“…The fragmentbased de novo design method, Optimum Packing of Molecular Fragments (OPMF), was used as the traditional drug design method. 12) After characterizing the target cavity of the protein, we generated 23 abstract structures. From these abstract structures, 3759 compounds were designed.…”

Section: Methodsmentioning

confidence: 99%

Section: -11)mentioning

confidence: 99%

“…In the previous study, 12) we employed the alchemical binding free energy evaluation method, Massively Parallel Computation of Absolute binding Free Energy (MP-CAFEE), 13,14) and showed that the empirical score (the interaction energy between a ligand and its target protein) is not strongly correlated with the calculated binding free energy. Because the molecular dynamics (MD) trajectories started from the same binding pose as in the empirical scoring, the calculated free energy and empirical score were compared in the same pose; thus, the result directly indicated the inaccuracy of the empirical scoring (see ref.…”

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The Feasibility of an Efficient Drug Design Method with High-Performance Computers

et al. 2015

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In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.Key words computational drug design; molecular dynamics; binding free energy; high-performance computing; force field parameter As predicted by the Moore's law, 1) computational power progresses exponentially each year. The K computer (RIKEN, Japan) was the first one to reach the computational speed of 10 petaflops (PF).2) Subsequently, the United States and China released 10 PF-scale supercomputers. Thus, such huge computational power is expected to be utilized effectively for progress in a wide variety of science and technology fields. The ultimate purpose of this study is to develop and demonstrate an efficient drug design method with the help of such a stateof-the-art supercomputer.To overcome the difficulty of drug development, many computational structure-based drug design (SBDD) methods have been proposed in the last two decades. One important advantage of these computational methods is that they are free of experimental difficulty; thus, the computational methods are expected to reduce the experimental effort involved in the SBDD. The in silico SBDD methods can be categorized into two groups: virtual screening [3][4][5][6][7][8] and de novo drug design. 9-11)In virtual screening method, drug candidates are selected from libraries of chemical compounds by predicting their binding free energies approximately. In de novo drug de...

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Section: Resultsmentioning

confidence: 99%

“…To demonstrate the feasibility of the binding free energy prediction procedure, we adopted the same target protein as that in ref. 12 ( Fig. 2).…”

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Section: -11)mentioning

confidence: 99%

mentioning

confidence: 99%

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The Feasibility of an Efficient Drug Design Method with High-Performance Computers

et al. 2015

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Basics and Recent Advances in Computational and Theoretical Methods for Understanding the Liquid–Liquid Phase Separation

Yamashita

2023

Phase Separation in Living Cells

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Statistical Mechanics of Ligand–Receptor Noncovalent Association, Revisited: Binding Site and Standard State Volumes in Modern Alchemical Theories

Procacci

Chelli

2017

J. Chem. Theory Comput.

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The present paper is intended to be a comprehensive assessment and rationalization, from a statistical mechanics perspective, of existing alchemical theories for binding free energy calculations of ligand−receptor systems. In detail, the statistical mechanics foundation of noncovalent interactions in ligand−receptor systems is revisited, providing a unifying treatment that encompasses the most important variants in the alchemical approaches from the seminal double annihilation method [Jorgensen et al. J. Chem. Phys. 1988; 89, 3742] ■ INTRODUCTIONThe determination of the binding free energy in ligand− receptor systems is the cornerstone of drug discovery. In the last few decades, traditional molecular docking techniques in computer-assisted drug design have been modified, integrated, or superseded using methodologies relying on a more realistic description of the drug−receptor system. It has become increasingly clear that a microscopic description of the solvent is a crucial ingredient to reliably rank the affinity of putative ligands for a given target. 1,2 In the framework of atomistic molecular dynamics (MD) simulations with explicit solvent, several methods for rigorously determining the absolute binding free energy in noncovalently bonded systems have been devised. Most of these methodologies are based on the socalled alchemical route 3−5 (see refs 6−9 for recent reviews). In this approach, proposed for the first time by Jorgensen et al. 10,11 and named double annihilation method (DAM), the absolute binding free energy of the ligand−receptor system was obtained by setting up a thermodynamic cycle as indicated in Figure 1 in which the basic quantities to be estimated are the annihilation or decoupling free energies of the ligand in the bound state and in bulk water, indicated hereafter as ΔG b and ΔG u , respectively. These decoupling free energies correspond to the two closing branches of the cycle and are obtained by discretizing the alchemical path connecting the fully interacting and fully decoupled ligand in a number of intermediate nonphysical states and then running MD simulations for each of these states.Alchemical states are defined by a λ coupling parameter entering the Hamiltonian, varying between 1 and 0, such that at λ = 1 and λ = 0 one has the fully interacting and gas-phase ligand, respectively. ΔG b and ΔG u are usually recovered as a sum of the contributions from each of the λ windows by applying the free energy perturbation method 12 (FEP). Alternatively, and equivalently, one can compute the canonical average of the derivative of the Hamiltonian at the discrete λ points, obtaining the decoupling free energy via numerical thermodynamic integration (TI). 13 Finally, the thermodynamic cycle is closed by computing the difference between the two decoupling free energies along the alchemical path, ΔG b and ΔG u , obtaining the dissociation free energy in solution.Gilson et al. 14 criticized Jorgensen's theory 10 by pointing out that the resulting dissociation free energy does not de...

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Molecular Dynamics Simulation-Based Evaluation of the Binding Free Energies of Computationally Designed Drug Candidates: Importance of the Dynamical Effects

Cited by 13 publications

References 33 publications

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

Basics and Recent Advances in Computational and Theoretical Methods for Understanding the Liquid–Liquid Phase Separation

Statistical Mechanics of Ligand–Receptor Noncovalent Association, Revisited: Binding Site and Standard State Volumes in Modern Alchemical Theories

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