Simulation of coarse-grained protein-protein interactions with graphics processing units

Journal of Chemical Theory and Computation

Volumn 6, Issue 11, 2010, Pages 3588-3600

  Tunbridge, Ian ^a   Best, Robert B ^b   Gain, James ^a   Kuttel, Michelle M ^a  

^a UNIVERSITY OF CAPE TOWN   (South Africa)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78149432360     PISSN: 15499618     EISSN: 15499626     Source Type: Journal    
DOI: 10.1021/ct1003884     Document Type: Article

References (37)

1. Tramontano, A. Problem 7. In The Ten Most Wanted Solutions in Protein Bioinformatics; Chapman & Hall/CRC mathematical biology and medicine series; Etheridge, A., Gross, L., Lenhart, S., Maini, P., Safer, H., and Voit, E., Eds.; CRC Press: Boca Raton, FL, 2005; pp 117 - 139.
2. Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: an overview of search algorithms and a guide to scoring functions Proteins 2002, 47, 409-443
3. Levitt, M. A simplified representation of protein conformations for rapid simulation of protein folding J. Mol. Biol. 1976, 104, 59-107
4. Friedrichs, M. S.; Wolynes, P. G. Toward protein tertiary structure recognition by means of associative memory Hamiltonians Science 1989, 246, 371-373
5. Shaknovich, E. I.; Gutin, A. M. Implications of thermodynamics of protein folding for evolution of primary sequences Nature 1990, 346, 773-775
6. Skolnick, J.; Kolinski, A. Simulations of the folding of a globular protein Science 1990, 250, 1121-1125
7. Karanicolas, J.; Brooks, C. L., III The origins of asymmetry in the folding transition states of protein L and protein G Protein Sci. 2002, 11, 2351-2361
8. Buchete, N.-V.; Straub, J. E.; Thirumalai, D. Anisotropic coarse-grained statistical potentials improve the ability to identify nativelike protein structures J. Chem. Phys. 2003, 118, 7658-7671
9. Tozzini, V. Coarse-grained models for proteins Curr. Opin. Struct. Biol. 2005, 15, 144-150
10. Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H. The MARTINI force field: coarse grained model for biomolecular simulations J. Phys. Chem. B 2007, 111, 7812-7824
11. Kim, Y. C.; Hummer, G. Coarse-grained Models for Simulations of Multiprotein Complexes: Application to Ubiquitin Binding J. Mol. Biol. 2008, 375, 1416-1433
12. Kirk, D. B.; Hwu, W.-M. W. Performance Considerations. In Programming Massively Parallel Processors: A Hands-on Approach, 1st ed.; Morgan Kaufmann: Burlington, MA, 2010.
13. Tomov, S.; Nath, R.; Ltaief, H.; Dongarra, J. Dense Linear Algebra Solvers for Multicore with GPU Accelerators. In Proceedings of IPDPS '10; Sigarch, A., Ed.; IEEE Computer Society Press: Washington, D.C., 2010.
14. Nyland, L.; Harris, M.; Prins, J. Fast N-Body Simulation with CUDA. In GPU Gems 3; Nguyen, H., Ed.; Addison Wesley Professional: Reading, MA, 2007; pp 677 - 695.
15. Belleman, R. G.; Bedorf, J.; Zwart, S. P. High Performance Direct Gravitational N-body Simulations on Graphics Processing Units II: An implementation in CUDA New Astron. 2007, 13, 103-112
16. Friedrichs, M. S.; Eastman, P.; Vaidyanathan, V.; Houston, M.; Legrand, S.; Beberg, A. L.; Ensign, D. L.; Bruns, C. M.; Pande, V. S. Accelerating molecular dynamic simulation on graphics processing units J. Comput. Chem. 2009, 30, 864-872
17. Stone, J. E.; Phillips, J. C.; Freddolino, P. L.; Hardy, D. J.; Trabuco, L. G.; Schulten, K. Accelerating Molecular Modeling Applications with Graphics Processors J. Comput. Chem. 2007, 28, 2618-2640
18. Rodrigues, C. I.; Hardy, D. J.; Stone, J. E.; Schulten, K.; Mei, W. GPU acceleration of cutoff pair potentials for molecular modeling applications. In Proceedings of the 5th conference on Computing frontiers (CF'08); ACM: New York, 2008; pp 273 - 282.
19. Hardy, D. J.; Stone, J. E.; Schulten, K. Multilevel summation of electrostatic potentials using graphics processing units Parallel Comput. 2009, 35, 164-177
20. Schmid, N.; Bötschi, M.; van Gunsteren, W. F. A GPU Solvent-Solvent Interaction Calculation Accelerator for Biomolecular Simulations Using the GROMOS Software J. Comput. Chem. 2010, 31, 1636-1643
21. CUDA Programming Guide, version 2.3; NVIDIA Corporation: Santa Clara, CA, 2009; http://developer.download.nvidia.com/compute/cuda/2/toolkit/docs/ NVIDIAUDArogramminguide.3.pdf. Accessed: August 19, 2010.
22. NVIDIA, CUDA Best Practices Guide 2.3; NVIDIA Corporation: Santa Clara, CA, 2009; http://developer.download.nvidia.com/compute/cuda/2\-3/toolkit/docs/ NVIDIA\-CUDA\-BestPracticesGuide\-2.3.pdf. Accessed: August 19, 2010.
23. Mackerell, A. D.; Feig, M.; Brooks, C. L. Empirical force fields for biological macromolecules: overview and issues J. Comput. Chem. 2004, 25, 1584-1604
24. Fraczkiewicz, R.; Braun, W. Exact and efficient analytical calculation of the accessible surface areas and their gradients for macromolecules J. Comput. Chem. 1998, 19, 319-333
25. Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; Teller, E. Equation of State Calculations by Fast Computing Machines J. Chem. Phys. 1953, 21, 1087-1092
26. Hastings, W. K. Monte Carlo Sampling Methods Using Markov Chains and Their Applications Biometrika 1970, 57, 97-109
27. Sugita, Y.; Okamoto, Y. Replica-exchange molecular dynamics methods for protein folding Chem. Phys. Lett. 1999, 314, 141-151
28. Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. CHARMM: A Program for MacroMolecular Energy, Minimization and Dynamics Calculations J. Comput. Chem. 1983, 4, 187-217
29. Swanson, K. A.; Kang, R. S.; Stamenova, S. D.; Hicke, L.; Radhakrishnan, I. Solution structure of Vps27 UIM-ubiquitin complex important for endosomal sorting and receptor downregulation EMBO J. 2003, 22, 4597-4606
30. Pelletier, H.; Kraut, J. Crystal structure of a complex between electron transfer partners, cytochrome c peroxidase and cytochrome c Science 1992, 258, 1748-1755
31. Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation J. Chem. Theory Comput. 2008, 4, 435-447
32. Matsumoto, M.; Nishimura, T. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator ACM Trans. Model. Comput. Simul. 1998, 8, 3-30
33. Harris, M.. Optimizing Parallel Reduction in CUDA; NVIDIA Corporation: Santa Clara, CA, 2007; http://developer.download.nvidia.com/compute/cuda/1-1/ Website/projects/reduction/doc/reduction.pdf. Accessed August 19, 2010.
34. nVIDIA's Next Generation CUDA Compute Architecture: Fermi. 2010; NVIDIA Corporation: Santa Clara, CA, 2010; http://www.nvidia.com/content/PDF/fermi- white-papers/NVIDIA-Fermi-Compute-Architecture-Whitepaper.pdf. Accessed August 19, 2010.
35. van Meel, J. A.; Arnold, A.; Frenkel, D.; Portegies; Belleman, R. G. Harvesting graphics power for MD simulations Mol. Simul. 2008, 34, 259-266
36. Tomov, S.; McGuigan, M.; Bennett, R.; Smith, G.; Spiletic, J. Benchmarking and implementation of probability-based simulations on programmable graphics cards Comput. Graph. 2005, 29, 71-80
37. nVIDIA OpenCL Jumpstart Guide; NVIDIA Corporation: Santa Clara, CA, 2009; http://developer.download.nvidia.com/OpenCL/NVIDIA-OpenCL-JumpStart-Guide.pdf. Accessed August 18, 2010.