Graphical Processing Unit accelerated Poisson equation solver and its application for calculation of single ion potential in ion-channels

Nikolay A. Simakov; Maria G. Kurnikova

Molecular Based Mathematical Biology (2013)

  • Volume: 1, page 151-163
  • ISSN: 2299-3266

Abstract

top
Poisson and Poisson-Boltzmann equations (PE and PBE) are widely used in molecular modeling to estimate the electrostatic contribution to the free energy of a system. In such applications, PE often needs to be solved multiple times for a large number of system configurations. This can rapidly become a highly demanding computational task. To accelerate such calculations we implemented a graphical processing unit (GPU) PE solver described in this work. The GPU solver performance is compared to that of our central processing unit (CPU) implementation of the solver. During the performance analysis the following three characteristics were studied: (1) precision associated with the modeled system discretization on the grid, (2) numeric precision associated with the floating point representation of real numbers (this is done via comparison of calculations with single precision (SP) and double precision (DP)), and (3) execution time. Two types of example calculations were carried out to evaluate the solver performance: (1) solvation energy of a single ion and a small protein (lysozyme), and (2) a single ion potential in a large ion-channel (α-hemolysin). In addition, influence of various boundary condition (BC) choices was analyzed, to determine the most appropriate BC for the systems that include a membrane, typically represented by a slab with the dielectric constant of low value. The implemented GPU PE solver is overall about 7 times faster than the CPU-based version (including all four cores). Therefore, a single computer equipped with multiple GPUs can offer a computational power comparable to that of a small cluster. Our calculations showed that DP versions of CPU and GPU solvers provide nearly identical results. SP versions of the solvers have very similar behavior: in the grid scale range of 1-4 grids/Å the difference between SP and DP versions is less than the difference stemming from the system discretization. We found that for the membrane protein, the use of a focusing technique with periodic boundary conditions in rough grid provides significantly better results than using a focusing technique with the electric potential set to zero at the boundaries.

How to cite

top

Nikolay A. Simakov, and Maria G. Kurnikova. "Graphical Processing Unit accelerated Poisson equation solver and its application for calculation of single ion potential in ion-channels." Molecular Based Mathematical Biology 1 (2013): 151-163. <http://eudml.org/doc/266786>.

@article{NikolayA2013,
abstract = {Poisson and Poisson-Boltzmann equations (PE and PBE) are widely used in molecular modeling to estimate the electrostatic contribution to the free energy of a system. In such applications, PE often needs to be solved multiple times for a large number of system configurations. This can rapidly become a highly demanding computational task. To accelerate such calculations we implemented a graphical processing unit (GPU) PE solver described in this work. The GPU solver performance is compared to that of our central processing unit (CPU) implementation of the solver. During the performance analysis the following three characteristics were studied: (1) precision associated with the modeled system discretization on the grid, (2) numeric precision associated with the floating point representation of real numbers (this is done via comparison of calculations with single precision (SP) and double precision (DP)), and (3) execution time. Two types of example calculations were carried out to evaluate the solver performance: (1) solvation energy of a single ion and a small protein (lysozyme), and (2) a single ion potential in a large ion-channel (α-hemolysin). In addition, influence of various boundary condition (BC) choices was analyzed, to determine the most appropriate BC for the systems that include a membrane, typically represented by a slab with the dielectric constant of low value. The implemented GPU PE solver is overall about 7 times faster than the CPU-based version (including all four cores). Therefore, a single computer equipped with multiple GPUs can offer a computational power comparable to that of a small cluster. Our calculations showed that DP versions of CPU and GPU solvers provide nearly identical results. SP versions of the solvers have very similar behavior: in the grid scale range of 1-4 grids/Å the difference between SP and DP versions is less than the difference stemming from the system discretization. We found that for the membrane protein, the use of a focusing technique with periodic boundary conditions in rough grid provides significantly better results than using a focusing technique with the electric potential set to zero at the boundaries.},
author = {Nikolay A. Simakov, Maria G. Kurnikova},
journal = {Molecular Based Mathematical Biology},
keywords = {Poisson-Boltzmann Equation Solver; Single Ion Potential; Solvation Energy; Lysozyme; α-Hemolysin; Poisson-Boltzmann equation solver; single ion potential; solvation energy; lysozyme; -hemolysin},
language = {eng},
pages = {151-163},
title = {Graphical Processing Unit accelerated Poisson equation solver and its application for calculation of single ion potential in ion-channels},
url = {http://eudml.org/doc/266786},
volume = {1},
year = {2013},
}

TY - JOUR
AU - Nikolay A. Simakov
AU - Maria G. Kurnikova
TI - Graphical Processing Unit accelerated Poisson equation solver and its application for calculation of single ion potential in ion-channels
JO - Molecular Based Mathematical Biology
PY - 2013
VL - 1
SP - 151
EP - 163
AB - Poisson and Poisson-Boltzmann equations (PE and PBE) are widely used in molecular modeling to estimate the electrostatic contribution to the free energy of a system. In such applications, PE often needs to be solved multiple times for a large number of system configurations. This can rapidly become a highly demanding computational task. To accelerate such calculations we implemented a graphical processing unit (GPU) PE solver described in this work. The GPU solver performance is compared to that of our central processing unit (CPU) implementation of the solver. During the performance analysis the following three characteristics were studied: (1) precision associated with the modeled system discretization on the grid, (2) numeric precision associated with the floating point representation of real numbers (this is done via comparison of calculations with single precision (SP) and double precision (DP)), and (3) execution time. Two types of example calculations were carried out to evaluate the solver performance: (1) solvation energy of a single ion and a small protein (lysozyme), and (2) a single ion potential in a large ion-channel (α-hemolysin). In addition, influence of various boundary condition (BC) choices was analyzed, to determine the most appropriate BC for the systems that include a membrane, typically represented by a slab with the dielectric constant of low value. The implemented GPU PE solver is overall about 7 times faster than the CPU-based version (including all four cores). Therefore, a single computer equipped with multiple GPUs can offer a computational power comparable to that of a small cluster. Our calculations showed that DP versions of CPU and GPU solvers provide nearly identical results. SP versions of the solvers have very similar behavior: in the grid scale range of 1-4 grids/Å the difference between SP and DP versions is less than the difference stemming from the system discretization. We found that for the membrane protein, the use of a focusing technique with periodic boundary conditions in rough grid provides significantly better results than using a focusing technique with the electric potential set to zero at the boundaries.
LA - eng
KW - Poisson-Boltzmann Equation Solver; Single Ion Potential; Solvation Energy; Lysozyme; α-Hemolysin; Poisson-Boltzmann equation solver; single ion potential; solvation energy; lysozyme; -hemolysin
UR - http://eudml.org/doc/266786
ER -

References

top
  1. [1] J. Antosiewicz, J. A. McCammon, and M. K. Gilson, “Prediction of pH-dependent properties of proteins.,” Journal of Molecular Biology, vol. 238, no. 3, pp. 415–436, 1994. 
  2. [2] D. Bashford and M. Karplus, “pKa’s of ionizable groups in proteins: atomic detail from a continuum electrostatic model.,” Biochemistry, vol. 29, no. 44, pp. 10219–10225, 1990. 
  3. [3] S. Bhakdi and J. Tranum-Jensen, “Alpha-toxin of Staphylococcus aureus.,” Microbiological reviews, vol. 55, no. 4, pp. 733–751, 1991. 
  4. [4] P. M. De Biase, C. J. F. Solano, S. Markosyan, L. Czapla, and S. Y. Noskov, “BROMOC-D: Brownian Dynamics/Monte-Carlo Program Suite to Study Ion and DNA Permeation in Nanopores.,” Journal of Chemical Theory and Computation, vol. 8, no. 7, pp. 2540–2551, 2012. 
  5. [5] N. Carrascal and D. F. Green, “Energetic decomposition with the generalized-born and Poisson-Boltzmann solvent models: lessons from association of G-protein components.,” The Journal of Physical Chemistry B, vol. 114, no. 15, pp. 5096–5116, 2010. [WoS] 
  6. [6] I.-L. Chern, J.-G. Liu, and W.-C. Wang, “Accurate evaluation of electrostatics for macromolecules in solution,” Methods and Applications of Analysis, vol. 10, no. 2, pp. 309–328, 2003. Zbl1099.92500
  7. [7] R. D. Coalson and M. G. Kurnikova, “Poisson-Nernst-Planck theory approach to the calculation of current through biological ion channels.,” IEEE Transactions on NanoBioscience, vol. 4, no. 1, pp. 81–93, 2005. 
  8. [8] M. L. Connolly, “Molecular Surfaces 5. Solvent Accessible Surfaces,” Network Science Corporation. [Online]. Available: http://www.netsci.org/Science/Compchem/feature14e.html. 
  9. [9] B. Eisenberg, “From Structure to Function in Open Ionic Channels,” The Journal of membrane biology, vol. 171, no. 1, pp. 1–24, 2010. 
  10. [10] M. K. Gilson and B. H. Honig, “Energetics of charge-charge interactions in proteins.,” Proteins, vol. 3, no. 1, pp. 32–52, Jan. 1988. [WoS] 
  11. [11] D. F. Green and B. Tidor, “Evaluation of electrostatic interactions.,” Current protocols in bioinformatics editoral board Andreas D Baxevanis et al, vol. Chapter 8, p. Unit 8.3, 2003. 
  12. [12] J. G. Kirkwood, “Theory of solutions of molecules containing widely separated charges with special application to zwitterions,” Journal of Chemical Physics, vol. 2, no. 7, pp. 351–361, 1934. [Crossref] Zbl0009.27504
  13. [13] I. Klapper, R. Hagstrom, R. Fine, K. Sharp, and B. Honig, “Focusing of electric fields in the active site of Cu-Zn superoxide dismutase: effects of ionic strength and amino-acid modification.,” Proteins, vol. 1, no. 1, pp. 47–59, 1986. 
  14. [14] V. Krishnamurthy and S.-H. C. S.-H. Chung, “Brownian dynamics simulation for modeling ion permeation across bionanotubes.,” IEEE Transactions on NanoBioscience, vol. 4, no. 1, pp. 102–111, 2005. [Crossref] 
  15. [15] B. Kuhn and P. A. Kollman, “Binding of a Diverse Set of Ligands to Avidin and Streptavidin: An Accurate Quantitative Prediction of Their Relative Affinities by a Combination of Molecular Mechanics and Continuum Solvent Models,” Journal of Medicinal Chemistry, vol. 43, no. 20, pp. 3786–3791, Oct. 2000. 
  16. [16] B. Lu, M. J. Holst, J. A. McCammon, and Y. C. Zhou, “Poisson-Nernst-Planck equations for simulating biomolecular diffusion-reaction processes I: Finite element solutions,” Journal of Computational Physics, vol. 229, no. 19, pp. 6979–6994, 2010. Zbl1195.92004
  17. [17] A. Nicholls and B. Honig, “A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation,” Journal of Computational Chemistry, vol. 12, no. 4, pp. 435–445, 1991. [Crossref] 
  18. [18] NVIDIA, NVIDIA CUDA C Programming Guide, PG-02829-001_v5.0, no. 350. NVIDIA Corporation, 2012. 
  19. [19] G. Prévost, L. Mourey, D. A. Colin, and G. Menestrina, “Staphylococcal pore-forming toxins.,” Current Topics in Microbiology and Immunology, vol. 257, pp. 53–83, 2001. 
  20. [20] M. Ramanadham, L. C. Sieker, and L. H. Jensen, “Refinement of triclinic lysozyme: II. The method of stereochemically restrained least squares.,” Acta Crystallographica Section B Structural Science, vol. 46, no. 1, pp. 63–69, 1990. 
  21. [21] W. Rocchia, S. Sridharan, A. Nicholls, E. Alexov, A. Chiabrera, and B. Honig, “Rapid grid-based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: applications to the molecular systems and geometric objects.,” Journal of Computational Chemistry, vol. 23, no. 1, pp. 128–137, 2002. [Crossref] 
  22. [22] B. Roux, “Theoretical and computational models of ion channels.,” Current Opinion in Structural Biology, vol. 12, no. 2, pp. 182–189, 2002. [Crossref] 
  23. [23] N. A. Simakov and M. G. Kurnikova, “Soft wall ion channel in continuum representation with application to modeling ion currents in α-hemolysin.,” The journal of physical chemistry. B, vol. 114, no. 46, pp. 15180–90, Dec. 2010. 
  24. [24] D. Sitkoff, K. A. Sharp, and B. Honig, “Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models,” Journal of Physical Chemistry, vol. 98, no. 7, pp. 1978–1988, 1994. 
  25. [25] L. Song, M. R. Hobaugh, C. Shustak, S. Cheley, H. Bayley, and J. E. Gouaux, “Structure of staphylococcal alphahemolysin, a heptameric transmembrane pore.,” Science, vol. 274, no. 5294, pp. 1859–1866, 1996. 
  26. [26] Y. Song, J. Mao, and M. R. Gunner, “MCCE2: Improving protein pKa calculations with extensive side chain rotamer sampling,” Journal of Computational Chemistry, vol. 30, no. 14, pp. 2231–2247, 2009. [WoS] 
  27. [27] J. Srinivasan, T. E. Cheatham, P. Cieplak, P. A. Kollman, and D. A. Case, “Continuum Solvent Studies of the Stability of Continuum Solvent Studies of the Stability of DNA , RNA , and Phosphoramidate-DNA Helices,” Structure, vol. 120, no. 37, pp. 9401–9409, 1998. 
  28. [28] D. J. Vocadlo, G. J. Davies, R. Laine, and S. G. Withers, “Catalysis by hen egg-white lysozyme proceeds via a covalent intermediate,” Nature, vol. 412, no. 6849, pp. 835–838, 2001. 
  29. [29] Y. C. Zhou, M. Feig, and G. W. Wei, “Highly accurate biomolecular electrostatics in continuum dielectric environments.,” Journal of Computational Chemistry, vol. 29, no. 1, pp. 87–97, 2008. [WoS] 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.