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Article
Multiscale Geometric Modeling of Macromolecules I: Cartesian Representation
Journal of Computational Physics (2014)
  • Kelin Xia, Michigan State University
  • Xin Feng, Michigan State University
  • Zhan Chen, Georgia Southern University
  • Yiying Tong, Michigan State University
  • Guo-Wei Wei, Michigan State University
Abstract
This paper focuses on the geometric modeling and computational algorithm development of biomolecular structures from two data sources: Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in the Eulerian (or Cartesian) representation. Molecular surface (MS) contains non-smooth geometric singularities, such as cusps, tips and self-intersecting facets, which often lead to computational instabilities in molecular simulations, and violate the physical principle of surface free energy minimization. Variational multiscale surface definitions are proposed based on geometric flows and solvation analysis of biomolecular systems. Our approach leads to geometric and potential driven Laplace–Beltrami flows for biomolecular surface evolution and formation. The resulting surfaces are free of geometric singularities and minimize the total free energy of the biomolecular system. High order partial differential equation (PDE)-based nonlinear filters are employed for EMDB data processing. We show the efficacy of this approach in feature-preserving noise reduction. After the construction of protein multiresolution surfaces, we explore the analysis and characterization of surface morphology by using a variety of curvature definitions. Apart from the classical Gaussian curvature and mean curvature, maximum curvature, minimum curvature, shape index, and curvedness are also applied to macromolecular surface analysis for the first time. Our curvature analysis is uniquely coupled to the analysis of electrostatic surface potential, which is a by-product of our variational multiscale solvation models. As an expository investigation, we particularly emphasize the numerical algorithms and computational protocols for practical applications of the above multiscale geometric models. Such information may otherwise be scattered over the vast literature on this topic. Based on the curvature and electrostatic analysis from our multiresolution surfaces, we introduce a new concept, the polarized curvature, for the prediction of protein binding sites.
Keywords
  • Protein characterization,
  • Variational multiscale surfaces,
  • Curvature analysis,
  • High order geometric PDEs,
  • Free energy functional,
  • EMDataBank,
  • Protein data bank
Disciplines
Publication Date
January 15, 2014
DOI
10.1016/j.jcp.2013.09.034
Publisher Statement
This is an Accepted Author Manuscript obtained from PMC. The publisher's final edited version of this article is available at Journal of Computational Chemistry.
Citation Information
Kelin Xia, Xin Feng, Zhan Chen, Yiying Tong, and Guo-Wei Wei. "Multiscale Geometric Modeling of Macromolecules I: Cartesian Representation" Journal of Computational Physics 257.A (2014): 912-936.
source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3855405/
doi:10.1016/j.jcp.2013.09.034
Available at: http://works.bepress.com/zhan_chen/2