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GPU Accelerated Finding of Channels and Tunnels for a Protein Molecule

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Abstract

This paper proposes a novel method for computing the cavities and channels/tunnels in a protein molecule in interactive time without significant user effort. A sphere tree structure is used to represent a protein molecule, which provides a parallel architecture to access a Graphic Processing Unit (GPU) memory. The use of CUDA programming with a GPU then allows the proposed system to work in parallel on either a sphere tree structure of a molecule or a set of voxels composing the space. A real-time performance is achieved for proximity queries on a protein molecule, and an interactive time performance is realized for finding all the cavities and channel/tunnels without user effort. The proposed system also provides a method for approximating a convex hull of a molecule in a discrete space, and then generates the shortest path from a user selected or automatically chosen cavity to the exterior of the protein molecule. Experimental results in comparison with previous methods confirm the time efficiency of the proposed system.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea

    Byungjoo Kim & Ku-Jin Kim

  2. Samsung Electronics, Suwon, Korea

    Jung Eun Lee

  3. Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea

    Young J. Kim

Authors
  1. Byungjoo Kim
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  2. Jung Eun Lee
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  3. Young J. Kim
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  4. Ku-Jin Kim
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Corresponding author

Correspondence to Ku-Jin Kim.

Additional information

Ku-Jin Kim was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2A10004391). Young J. Kim was supported in part by NRF in Korea (No. 2012R1A2A2A01046246, No. 2012R1A2A2A06047007) and MCST/KOCCA in the CT R&D program 2014 (R2014060011).

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Kim, B., Lee, J.E., Kim, Y.J. et al. GPU Accelerated Finding of Channels and Tunnels for a Protein Molecule. Int J Parallel Prog 44, 87–108 (2016). https://doi.org/10.1007/s10766-014-0331-8

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  • DOI: https://doi.org/10.1007/s10766-014-0331-8

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