Efficient GPU-based parallelization of solvation calculation for the blind docking problem

Abstract

Molecular docking techniques are widely used in computational drug discovery. Most of these techniques simulate the way that a ligand interacts with a protein target focusing on one binding site. Blind docking is a recent technique which is designed to search the entire surface of the protein to discover new interesting binding sites. Unfortunately, this new docking method is computationally more intensive since its complexity grows exponentially according to the number of binding sites, which severely limits its utilization in practice. This paper shows a road-map for an efficient parallelization of the calculation of the solvation energy which represents the most time-consuming part of the scoring function. The latter constitutes a bottleneck in both simple and blind docking. The proposed parallelization approach aims to efficiently exploit the large computing power offered by the latest NVIDIA GPU architectures. Toward this goal, we propose a new parallel approach that exploits the Hyper-Q capability to compute several GPU kernels simultaneously, thereby speedingup the computation process and maximizing the GPU utilization. The obtained results show the huge benefit of exploiting the latest GPU architectures as compared to serial and parallel CPU approaches. Indeed, for 100 binding sites, our results show an average speedup of 186\(\times\) as compared to the serial implementation, and 10\(\times\) as compared to the multi-core CPU version. Moreover, an experimental comparison shows the superiority of our approach over the state of the art.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Bleiweiss A (2008) Gpu accelerated pathfinding. In: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, Eurographics Association, pp 65–74

  2. 2.

    Bradley T (2012) Hyper-q example. NVidia Corporation. Whitepaper v1. 0

  3. 3.

    Eisenberg D, McLachlan AD (1986) Solvation energy in protein folding and binding. Nature 319(6050):199–203

    Article  Google Scholar 

  4. 4.

    Fang J, Varbanescu AL, Imbernon B, Cecilia JM, Sánchez HEP (2014) Parallel computation of non-bonded interactions in drug discovery: Nvidia gpus vs. intel xeon phi. In: IWBBIO, pp 579–588

  5. 5.

    Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular docking and structure-based drug design strategies. Molecules 20(7):13384–13421. https://doi.org/10.3390/molecules200713384, http://www.mdpi.com/1420-3049/20/7/13384

  6. 6.

    GPGPU: CUDA zone. https://developer.nvidia.com/cuda-zone

  7. 7.

    Green S (2010) Particle simulation using cuda. NVIDIA Whitepaper 6:121–128

    Google Scholar 

  8. 8.

    Hetenyi C, van der Spoel D (2006) Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett 580(5):1447–1450. https://doi.org/10.1016/j.febslet.2006.01.074

    Article  Google Scholar 

  9. 9.

    Hetényi C, van der Spoel D (2002) Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci 11(7):1729–1737. https://doi.org/10.1110/ps.0202302

    Article  Google Scholar 

  10. 10.

    Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801. https://doi.org/10.1021/jm0608356 PMID: 17154509

    Article  Google Scholar 

  11. 11.

    Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28(6):1145–1152

    Article  Google Scholar 

  12. 12.

    Imbernón B, Prades J, Giménez D, Cecilia JM, Silla F (2018) Enhancing large-scale docking simulation on heterogeneous systems: an mpi vs rcuda study. Future Gen Comput Syst 79:26–37

    Article  Google Scholar 

  13. 13.

    Kannan S, Ganji R (2010) Porting autodock to cuda. In: IEEE Congress on Evolutionary Computation, pp 1–8. https://doi.org/10.1109/CEC.2010.5586277

  14. 14.

    Kirk DB, Wen-Mei WH (2016) Programming massively parallel processors: a hands-on approach. Morgan Kaufmann, Burlington

    Google Scholar 

  15. 15.

    Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20(23):2839–2860

    Article  Google Scholar 

  16. 16.

    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ et al (1998) Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662

    Article  Google Scholar 

  17. 17.

    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and autodocktools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    Article  Google Scholar 

  18. 18.

    Nickolls J, Dally WJ (2010) The gpu computing era. IEEE Micro 30(2):56–69. https://doi.org/10.1109/MM.2010.41

    Article  Google Scholar 

  19. 19.

    NVIDIA: Pascal architecture. https://devblogs.nvidia.com/inside-pascal/

  20. 20.

    NVIDIA: Thrust. http://docs.nvidia.com/cuda/thrust/index.html

  21. 21.

    NVIDIA AUTOMOTIVE: Giving Cars the Power to See, Think, and Learn . http://www.nvidia.com/object/drive-automotive-technology.html

  22. 22.

    NVIDIA Science and Medical Imaging: Accelerating Science and Medical Imaging with NVIDIA GPUS -/science-and-medical. http://www.nvidia.com/object/science-and-medical-imaging.html

  23. 23.

    NVIDIA Whitepaper: NVIDIA Tesla P100 The Most Advanced Datacenter Accelerator Ever Built . https://images.nvidia.com/content/pdf/tesla/whitepaper/pascal-architecture-whitepaper.pdf

  24. 24.

    OpenMP Architecture Review Board (2017) The OpenMP Specification. http://www.openmp.org, (accessed, April, 2th, 2017)

  25. 25.

    Owens JD, Houston M, Luebke D, Green S, Stone JE, Phillips JC (2008) Gpu computing. Proc IEEE 96(5):879–899

    Article  Google Scholar 

  26. 26.

    Saadi H, Nouali-Taboudjemat N, Rahmoun A, Imbernón B, Peréz-Sánchez H, Cecilia JM (2017) Parallel desolvation energy term calculation for blind docking on gpu architectures. In: Parallel Processing Workshops (ICPPW), 2017 46th International Conference on, IEEE, pp 16–22

  27. 27.

    Sanders J, Kandrot E (2010) CUDA by example: an introduction to general-purpose GPU programming, portable documents. Addison-Wesley Professional, Boston

    Google Scholar 

  28. 28.

    Sukhwani B, Herbordt MC (2009) Gpu acceleration of a production molecular docking code. In: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units, ACM, pp 19–27

  29. 29.

    The Scripps Research Institute. TSRI: Desolvation Free Energy Term in AutoDock 4. http://autodock.scripps.edu/resources/science/autodock-4-desolvation-free-energy//

  30. 30.

    Zhang Q, Wang J, Guerrero GD, Cecilia JM, García JM, Li Y, Pérez-Sánchez H, Hou T (2013) Accelerated conformational entropy calculations using graphic processing units. J Chem Inform Model 53(8):2057–2064

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the Fundacin Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Projects 20988/PI/18, 20813/PI/18 and 20524/PDC/18, and by the grants from the Spanish Ministry of Economy and Competitiveness (TIN2016-80565-R and CTQ2017-87974-R). The authors thankfully acknowledge the computer resources at CTE-POWER and the technical support provided by Barcelona Supercomputing Center - Centro Nacional de Supercomputación (RES-BCV-2018-3-0008).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hocine Saadi.

Additional information

Publisher's Note

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Scoring function: A function used to approximately predict the binding affinity between two molecules when docking to each other.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Saadi, H., Nouali Taboudjemat, N., Rahmoun, A. et al. Efficient GPU-based parallelization of solvation calculation for the blind docking problem. J Supercomput 76, 1980–1998 (2020). https://doi.org/10.1007/s11227-019-02834-5

Download citation

Keywords

  • Molecular docking
  • Blind docking
  • Parallel computing
  • HPC
  • Emergent GPU architectures