GPU Accelerated Finding of Channels and Tunnels for a Protein Molecule

Byungjoo Kim, Jung Eun Lee, Young J. Kim, Ku Jin Kim

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)87-108
Number of pages22
JournalInternational Journal of Parallel Programming
Issue number1
StatePublished - 1 Feb 2016

Bibliographical note

Funding 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).

Publisher Copyright:
© 2014, Springer Science+Business Media New York.


  • Cavities
  • Channels
  • GPU programming
  • Protein molecules
  • Tunnels


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