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

Abstract

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...

Improvement in Empirical Potential Functions for Increasing the Utility of Molecular Dynamics Simulations

“…Previously, the FUJI force field was shown to improve the accuracy of binding free energy predictions [30,31]. To further demonstrate the accuracy of the FUJI force field, in the present study, the conformational distribution of the Ala dipeptide was investigated.…”

“…To that end, we have shown that MD simulations with the FUJI force field can accurately reproduce the conformational distribution of the Ala dipeptide as determined using IR and Raman spectroscopy [30,31]. In contrast, simulations using other force fields overestimated the distribution of the α R conformation.…”

“…For example, the correct equilibration is required to accurately calculate the free energy difference in free energy perturbation calculations. In fact, we have shown that the binding free energy calculation results for the ff99 force field are less accurate than those for the FUJI force field [30]. In this context, it is an important challenge to further optimize the force field parameters to improve the accuracy of MD simulations.…”

“…Another important advantage of the FUJI force field is that the protein parameters are consistent with those for small-molecule ligands, which is essential for calculating binding free energies. Therefore, one of our challenging projects involves the combined application of the successful binding free energy calculation method MP-CAFEE [28] and the FUJI force field to drug design [29,30].…”

Multiplicity in Long Noncoding RNA in Living Cells

“…In our on-going studies, MD simulations of larger RNA systems (including RNAprotein complexes) supplement additional valuable data. The accuracy of MD simulations is essentially dependent on the force field [18,19]. Recently, the force field of RNA has been improved significantly [20].…”