Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes

Tao Liao; Yongjie Zhang; Peter M. Kekenes-Huskey; Yuhui Cheng; Anushka Michailova; Andrew D. McCulloch; Michael Holst; J. Andrew McCammon

Molecular Based Mathematical Biology (2013)

  • Volume: 1, page 164-179
  • ISSN: 2299-3266

Abstract

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Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaussian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.

How to cite

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Tao Liao, et al. "Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes." Molecular Based Mathematical Biology 1 (2013): 164-179. <http://eudml.org/doc/266914>.

@article{TaoLiao2013,
abstract = {Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaussian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.},
author = {Tao Liao, Yongjie Zhang, Peter M. Kekenes-Huskey, Yuhui Cheng, Anushka Michailova, Andrew D. McCulloch, Michael Holst, J. Andrew McCammon},
journal = {Molecular Based Mathematical Biology},
keywords = {efficient computation; multi-scale modeling; biomolecular complex; mesh generation; multi-core CPU; GPU},
language = {eng},
pages = {164-179},
title = {Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes},
url = {http://eudml.org/doc/266914},
volume = {1},
year = {2013},
}

TY - JOUR
AU - Tao Liao
AU - Yongjie Zhang
AU - Peter M. Kekenes-Huskey
AU - Yuhui Cheng
AU - Anushka Michailova
AU - Andrew D. McCulloch
AU - Michael Holst
AU - J. Andrew McCammon
TI - Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes
JO - Molecular Based Mathematical Biology
PY - 2013
VL - 1
SP - 164
EP - 179
AB - Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaussian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.
LA - eng
KW - efficient computation; multi-scale modeling; biomolecular complex; mesh generation; multi-core CPU; GPU
UR - http://eudml.org/doc/266914
ER -

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