• Vol 9, No 1 (2018)
  • Electrical, Electronics, and Computer Engineering

The Performance of a Molecular Dynamics Simulation for the Plasmodium falciparum Enoyl-acyl carrier-protein Reductase Enzyme using Amber and GTX 780 and 970 Double Graphical Processing Units

Heru Suhartanto, Arry Yanuar, Ari Wibisono, Denny Hermawan, Alhadi Bustamam


Cite this article as:
Suhartanto, H., Yanuar, A., Wibisono, A., Hermawan, D., Bustamam, A., 2018. The Performance of a Molecular Dynamics Simulation for the Plasmodium falciparum Enoyl-acyl carrier-protein Reductase Enzyme using Amber and GTX 780 and 970 Double Graphical Processing Units. International Journal of Technology. Volume 9(1), pp. 150-158
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Heru Suhartanto Universitas Indonesia
Arry Yanuar Universitas Indonesia
Ari Wibisono Universitas Indonesia
Denny Hermawan Universitas Indonesia, Al Azhar Indonesia University
Alhadi Bustamam Universitas Indonesia
Email to Corresponding Author

Abstract
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The invention of graphical processing units (GPUs) has significantly improved the speed of long processes used in molecular dynamics (MD) to search for drug candidates to treat diseases, such as malaria. Previous work using a single GTX GPU showed considerable improvement compared to GPUs run in a cluster environment. In the current work, AMBER and dual GTX 780 and 970 GPUs were used to run an MD simulation on the Plasmodium falciparum enoyl-acyl carrier protein reductase enzyme; the results showed that performance was improved, particularly for molecules with a large number of atoms using single GPU.

GPU; Molecular dynamic simulation; PfENR

Conclusion

The results show that GPU specifications can be used to determine the performance of MD simulations, with the number of cores and memory clock rate being primary factors affecting simulation performance. Using AMBER and two GPUs for large proteins, such as PfENR (37,873 atoms) which is a potential target for antimalarial drugs, JAC (23,558 atoms), and myoglobin (2,492 atoms), can improve performance in ns/day by two-fold and reduce execution time compared to using a single GPU. For small proteins, such as TRP cage (304 atoms), using two GPUs does not improve performance. A significant improvement in simulation performance occurred for molecules that had more atoms because, to communicate between GPUs, the hardware must synchronize data from the first GPU, the CPU memory, the CPU, and the second GPU. If the amount of data is small, there will be overhead to this synchronization, but if the amount of data is large the synchronization process is more efficient, which improves simulation performance by reducing simulation time. In addition to inputting the size of the protein, the size of the threads per block also affects performance as measured by ns/day. Thus, a maximum thread value per block based on GPU specifications should be used to improve simulation performance. MD simulations can be performed in a public cloud-computing environment, in which all infrastructure requirements for the hardware and software have been provided by the service provider, including support tools to simplify the user interface. This is very helpful for researchers who do not have the ability to build the infrastructure, install the hardware or software, or configure system administration. Additionally, users do not have to solve problems in the infrastructure, such as power or cooling issues. Nevertheless, the hardware provided by the service provider, as well as the waiting time prior to execution, depends on the level of activity in the computing environment, which also affects the overall simulation time.

References

Agarwal, A.K., Fishwick, C.W.G., 2010. Structure-based Design of Anti-infectives. Annals of the New York Academy of Sciences, Volume 1213(1), pp. 20–45

Alam, S.R., Agarwal, P.K., Hampton, S.S., Ong, H., 2008. Experimental Evaluation of Molecular Dynamics Simulations on Multi-core Systems. In: Proceedings of the 15th International Conference on High Performance Computing, pp. 616–622

Amaro, R., Li, W., 2010. Emerging Methods for Ensemble-based Virtual Screening. Current Topics in Medicinal Chemistry, Volume 10(1), pp. 3–13

Amisaki, T., Fujiwara, S., 2004. Grid-enabled Applications in Molecular Dynamics Simulations using a Cluster of Dedicated Computers. In: International Symposium on Applications and the Internet Workshops (Saint 2004Workshop). Tokyo, Japan, 26 January 2004, pp. 616–622

Ben Mamoun, C., Prigge, S.T., Vial, H., 2009. Targeting the Lipid Metabolic Pathways for the Treatment of Malaria. Drug Development Research, Volume 71(1), pp. 44–55

Case, D.A., Cheatham, T.E., Darden, T., Gohlke, H., Luo, R., Merz, K.M., Onufriev, A., Simmerling, C., Wang, B., Woods, R.J., 2005. The AMBER Biomolecular Simulation Programs. Journal of Computational Chemistry, Volume 26(16), pp. 1668–1688

Chhibber, M., Kumar, G., Parasuraman, P., Ramya, T.N.C., Surolia, N., Surolia, A., 2006. Novel Diphenyl Ethers: Design, Docking Studies, Synthesis and Inhibition of Enoyl ACP Reductase of Plasmodium falciparum and Escherichia coli, Bioorganic & Medicinal Chemistry, Volume 14(23), pp. 8086–8098

Frecer, V., Megnassan, E., Miertus, S., 2009. Design and in silico Screening of Combinatorial Library of Antimalarial Analogs of Triclosan Inhibiting Plasmodium falciparum Enoyl-acyl Carrier Protein Reductase. European Journal of Medicinal Chemistry, Volume 44(7), pp. 3009–3019

FutureSystems, 2015. FutureSystems. Available online at: http://futuregrid.org

Jorgensen, W.L., Maxwell, D.S., Tirado-Rives, J., 1996. Development and Testing of the OPLS All-atom Force Field on Conformational Energetics and Properties of Organic Liquids. Journal of the American Chemical Society, Volume 118(45), pp. 11225–11236

Liu, X., Peng, S., Yang, C., Wu, C., Wang, H, Cheng, Q, Zhu, W, Wang, J., 2015. mAMBER: Accelerating Explicit Solvent Molecular Dynamic with Intel Xeon Phi Many-Integrated Core Coprocessors. In: Proceedings of the 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, pp. 729–732

LLC, A.P.C., 2012. New 4-day Training Course on GPU-enabled Neural Networks

MacKerell, A.D., Bashford, D., Bellott, M., Dunbrack, R.L., Evanseck, J.D., Field, M.J., Fischer, S., Gao, J., Guo, H., Ha, S., Joseph-McCarthy, D., Kuchnir, L., Kuczera, K., Lau, F.T.K., Mattos, C., Michnick, S., Ngo, T., Nguyen, D.T., Prodhom, B., Reiher, W.E., Roux, B., Schlenkrich, M., Smith, J.C., Stote, R., Straub, J., Watanabe, M., Wiórkiewicz-Kuczera, J., Yin, D., Karplus, M., 1998. All-atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. Journal of Physical Chemistry B, Volume 102(18), pp. 3586–3616

Mell, P., Grance, T., 2012. The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology Special Publication 800-145

Morris, G.M., 1989. AutoDock’s Role in Developing the First Clinically-approved HIV Integrase Inhibitor—AutoDock

Peng, S., Zhang, X., Lu, Y., Liao, X.-K., Lu, K., Yang, C., Liu, J., Zhu, W., Wei, D.-Q., 2016. mAMBER: A CPU/MIC Collaborated Parallel Framework for AMBER on Tianhe-2 Supercomputer. In: Proceeding of IEEE International Conference on Bioinformatics and Biomedicine, BIBM, Shenzhen, China, December 15-18, 2016, pp. 651–657

Suhartanto, H., Yanuar, A., Bustamam, A., Azizah, A.Y., Wibisono, A., Hilman, M., 2014. Performance Analysis of Molecular Dynamics Simulation of PfENR Enzyme using AMBER on Cluster and GPU Computing Environment. International Journal of Advancements in Computing Technology, Volume 6(1), pp. 68–78

Suhartanto, H., Yanuar, A., Hilman, M., Wibisono, A., Dermawan, T., 2012. Performance Analysis, Cluster Computing Environments on Molecular Dynamics Simulation of LOX-RAD GTPase and Curcumin Molecules with AMBER. International Journal of Computer Science Issues, Volume 9(2), pp. 90-96

Suhartanto, H., Yanuar, A., Wibisono, A., 2011. Performance Analysis Cluster and GPU Computing Environment on Molecular Dynamic Simulation of BRV-1 and REM2 with GROMAC. International Journal of Computer Science Issues, Volume 8(4), pp. 131-135

Tasdemir, D., 2006. Type II Fatty Acid Biosynthesis, a New Approach in Antimalarial Natural Product Discovery. Phytochemistry Reviews, Volume 5(1), pp. 99–108

XSEDE, 2011. XSEDE. Available online at: https://www.xsede.org/