Molecular dynamics simulations through GPU video games technologies

Styliani Loukatou, Louis Papageorgiou, Paraskevas Fakourelis, Arianna Filntisi, Eleftheria Polychronidou, Ioannis Bassis, Vasileios Megalooikonomou, Wojciech Makałowski, Dimitrios Vlachakis, Sophia Kossida


Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD).

MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU).

A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations.


Molecular Dynamics; GPGPU; CUDA;


Alder BJ & Wainwright TE 1959 Studies in molecular dynamics. I. General Method Journal of Chemical Physics 31 459-466

Alerstam E, Lo WC, Han TD, Rose J, Andersson-Engels S & Lilge L 2010 Next-generation acceleration and code optimization for light transport in turbid media using Gpus Biomed Opt Express 1 658-675

Almasi GS & Gottlieb A 1989 Highly parallel computing The Benjamin/Cummings series in computer science and engineering. CA, USA: Benjamin/Cummings

Asanovíc K, Bodik R, Catanzaro B, Gebis J, Husbands P, Keutzer K, Patterson D, Plishker W, Shalf J, Williams S & Yelick K 2006 The landscape of parallel computing research: a view from Berkeley Electrical Engineering and Computer Sciences, University of California at Berkeley

Attwood TK, Gisel A, Eriksson NE & Bongcam-Rudloff E 2011 Concepts, historical milestones and the central place of bioinformatics in Modern Biology: A European Perspective. Bioinformatics – Trends and Methodologies. Eds M Mahdavi. InTech

Browna WM, Kohlmeyerb A, Plimptonc SJ & Tharringtona AN 2012 Implementing molecular dynamics on hybrid high performance computers - particle–particle particle-mesh. Comp Phys Comm 183 449-459

Buch I, Harvey MJ, Giorgino T, Anderson DP & De Fabritiis G 2010 High-throughput all-atom molecular dynamics simulations using distributed computing. J Chem Inf Model 50 397-403

Che S, Boyer M, Meng J, Tarjan D, Sheaffer JE & Skadron K 2008 Performance study of general-purpose applications on graphics processors using Cuda J Parall Distr Comp 68 1370-1380

Fago A, D'Avino R & Di Prisco G 1992 The hemoglobins of notothenia angustata, a temperate fish belonging to a family largely endemic to the antarctic ocean Eur J Biochem 210 963-970

Hesper B & Hogeweg P 1970 Bioinformatica: Een Werkconcept Kameleon Kameleon 1 28-29

Hoang RV, Tanna D, Jayet Bray LC, Dascalu SM & Harris FC Jr 2013 A novel Cpu/Gpu simulation environment for large-scale biologically realistic neural modeling Front Neuroinform 7 19

Holk E, Byrd W, Mahajan N, Willcock J, Chauhan A & Lumsdaine A 2011 declarative parallel programming for Gpus. In 14th biennial ParCo edited by KD Bosschere, EH D'hollander, GR Joubert, D Padua, F Peters and M Sawyer. Amsterdam, Netherlands.

Hu G & Wang J 2014 Ligand selectivity of estrogen receptors by a molecular dynamics study Eur J Med Chem 74 726-735

Jayasundar JJ, Xing J, Robinson JM, Cheung HC & Dong WJ 2014 Molecular dynamics simulations of the cardiac troponin complex performed with fret distances as restraints. PLoS One 9 e87135

Johnson J & Vijayalakshmi G 2013 Comparison study of parallel computing with Aluand Gpu (Cuda). Int J Sci Res (IJSR) 2 209-212

Karplus M & McCammon JA 2002 Molecular dynamics simulations of biomolecules Nat Struct Biol 9 646-652

Kirk D & Hwu WM 2010 Programming Massively Parallel Processors Hands-on with Cuda. MA, USA: Morgan Kaufmann Publishers

Kulkarni AK & Ojha RP 2014 Combined 1 H-NMR and molecular dynamics studies on conformational behavior of a model heptapeptide, GRGDSPC. Chem Biol Drug Des

Lai ZB, Wang M, Yan C & Oloyede A 2014 Molecular dynamics simulation of mechanical behavior of osteopontin-hydroxyapatite interfaces. J Mech Behav Biomed Mater 36C 12-20

Lv Z, Tek A, Da Silva F, Empereur-mot C, Chavent M & Baaden M 2013 Game on, science - how video game technology may help biologists tackle visualization challenges. PLoS One 8 e57990

Mesirov JP, Schulten K & Sumners DW 1996 Mathematical Approaches to Biomolecular Structure and Dynamics. The Ima Volumes in Mathematics and Its Applications. New York, USA: Springer

Murakami T, Kasahara R & Saito T 2010 An implementation and its evaluation of password cracking tool parallelized on Gpgpu. IEEE 534 - 538

NVIDIA 2014 Cuda C Programming Guide. NVIDIA Corporation

NVIDIA Corporation. Cuda Gpus. NVIDIA Developer. Accessed from at June 6, 2014.

Owens JD, Houston M, Luebke D, Green S, Stone JE & Phillips JC 2008 Gpu computing. Proc IEEE 6

Pan L, Gu L & Xu J 2008 Implementation of medical image segmentation in Cuda. IEEE 82-85

Papageorgiou L, Vlachakis D, Koumandou VL, Papangelopoulos N & Kossida S 2013 Computer-aided drug design and biological evaluation of novel anti-Greek goat encephalitis agents. Int J Syst Biol Biomed Tech 2 1-16

Papangelopoulos N, Vlachakis D, Filntisi A, Fakourelis P, Papageorgiou L, Megalooikonomou V & Kossida S 2014 State of the art Gpgpu applications in bioinformatics. Int J Syst Biol Biomed Tech 2 24-48

Resch M 2010 High Performance Computing on Vector Systems 2009 New York, USA: Springer

Rudy G, Khan MM, Hall M, Chen C & Chame J 2011. A programming language interface to describe transformations and code generation. Lang Comp Parall Comp 6548 136-150

Scarpino M 2012 Opencl in Action : How to Accelerate Graphics and Computation. NY, USA: Manning

Sharma R, Gupta N, Narang V & Mittal A 2011 Parallel implementation of DNA sequences matching algorithms using pwm on Gpu architecture. Int J Bioinform Res Appl 7 202-215

Stone JE, Gohara D & Shi G 2010 Opencl: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12 66-72

Stone JE, Hardy DJ, Ufimtsev IS & Schulten K 2010 Gpu-accelerated molecular modeling coming of age. J Mol Graph Model 29 116-125

Stromme A, Carlson R & Newhall T 2012 Chestnut: A Gpu programming language for non-experts. PMAM

Ueng S, Lathara M, Baghsorkhi SS & Hwu WW 2008 Cuda-Lite: reducing Gpu programming complexity. Lang Comp Parall Comp 5335 1-15

Vlachakis D, Bencurova E, Papangelopoulos N & Kossida S 2014 Current state-of-the-art molecular dynamics methods and applications. Adv Protein Chem Struct Biol 94 269-313

Vlachakis D & Kossida S 2013 Molecular modeling and pharmacophore elucidation study of the classical swine fever virus helicase as a promising pharmacological target. PeerJ 1 e85

Vlachakis D, Tsagrasoulis D, Megalooikonomou V & Kossida S 2013 Introducing drugster: a comprehensive and fully integrated drug design, lead and structure optimization toolkit. Bioinformatics 29 126-128

Full Text: PDF


  • There are currently no refbacks.

Copyright © 2017 Lorem Ipsum Press