Liao Tao, Zhang Yongjie, Kekenes-Huskey Peter M, Cheng Yuhui, Michailova Anushka, McCulloch Andrew D, Holst Michael, McCammon J Andrew
Department of Mechanical Engineering, Carnegie Mellon University.
Departments of Chemistry and Pharmacology, University of California, San Diego.
Mol Based Math Biol. 2013 Jul;1. doi: 10.2478/mlbmb-2013-0009.
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 Gaus-sian 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.
多尺度建模在理解大型生物分子复合物的结构和生物学功能方面发挥着重要作用。在本文中,我们提出了一种高效的计算框架,用于从蛋白质数据库(PDB)中的原子分辨率数据构建多尺度模型,该框架由多核CPU和可编程图形处理单元(GPU)加速。采用高斯核函数的多级求和来生成生物分子的隐式模型。求和中的系数被设计为结构索引的函数,这些索引指定了特定层次的结构,并能够对生物分子表面进行局部分辨率控制。采用一种称为邻近搜索的方法来定位靠近预期生物分子表面的网格点,并减少要分析的网格数量。对于特定的网格点,应用KD树或包围体层次结构来搜索对其密度计算有贡献的原子,由于高斯核函数的衰减,远处的原子被忽略。除了构建密度图外,在网格生成和质量改进过程中还采用并比较了三种模式,以生成高质量的四面体网格:CPU顺序模式、多核CPU并行模式和GPU并行模式。我们已将我们的算法应用于几种大型蛋白质,并取得了良好的结果。