Literature DB >> 23813599

Multiscale geometric modeling of macromolecules II: Lagrangian representation.

Xin Feng1, Kelin Xia, Zhan Chen, Yiying Tong, Guo-Wei Wei.   

Abstract

Geometric modeling of biomolecules plays an essential role in the conceptualization of biolmolecular structure, function, dynamics, and transport. Qualitatively, geometric modeling offers a basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. Quantitatively, geometric modeling bridges the gap between molecular information, such as that from X-ray, NMR, and cryo-electron microscopy, and theoretical/mathematical models, such as molecular dynamics, the Poisson-Boltzmann equation, and the Nernst-Planck equation. In this work, we present a family of variational multiscale geometric models for macromolecular systems. Our models are able to combine multiresolution geometric modeling with multiscale electrostatic modeling in a unified variational framework. We discuss a suite of techniques for molecular surface generation, molecular surface meshing, molecular volumetric meshing, and the estimation of Hadwiger's functionals. Emphasis is given to the multiresolution representations of biomolecules and the associated multiscale electrostatic analyses as well as multiresolution curvature characterizations. The resulting fine resolution representations of a biomolecular system enable the detailed analysis of solvent-solute interaction, and ion channel dynamics, whereas our coarse resolution representations highlight the compatibility of protein-ligand bindings and possibility of protein-protein interactions.
Copyright © 2013 Wiley Periodicals, Inc.

Entities:  

Keywords:  curvature; electrostatics; energy functional; meshing; multiresolution surface; variational multiscale modeling

Mesh:

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Year:  2013        PMID: 23813599      PMCID: PMC3760017          DOI: 10.1002/jcc.23364

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  50 in total

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10.  Between algorithm and model: different Molecular Surface definitions for the Poisson-Boltzmann based electrostatic characterization of biomolecules in solution.

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  11 in total

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2.  Persistent homology analysis of protein structure, flexibility, and folding.

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3.  DG-GL: Differential geometry-based geometric learning of molecular datasets.

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Journal:  Int J Numer Method Biomed Eng       Date:  2019-02-07       Impact factor: 2.747

4.  Multiscale geometric modeling of macromolecules I: Cartesian representation.

Authors:  Kelin Xia; Xin Feng; Zhan Chen; Yiying Tong; Guo Wei Wei
Journal:  J Comput Phys       Date:  2014-01       Impact factor: 3.553

Review 5.  A review of mathematical representations of biomolecular data.

Authors:  Duc Duy Nguyen; Zixuan Cang; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-02-26       Impact factor: 3.676

6.  Multiscale geometric modeling of macromolecules II: Lagrangian representation.

Authors:  Xin Feng; Kelin Xia; Zhan Chen; Yiying Tong; Guo-Wei Wei
Journal:  J Comput Chem       Date:  2013-06-29       Impact factor: 3.376

7.  Object-oriented Persistent Homology.

Authors:  Bao Wang; Guo-Wei Wei
Journal:  J Comput Phys       Date:  2016-01-15       Impact factor: 3.553

8.  Multiscale method for modeling binding phenomena involving large objects: application to kinesin motor domains motion along microtubules.

Authors:  Lin Li; Joshua Alper; Emil Alexov
Journal:  Sci Rep       Date:  2016-03-18       Impact factor: 4.379

9.  Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

Authors:  Zixuan Cang; Lin Mu; Guo-Wei Wei
Journal:  PLoS Comput Biol       Date:  2018-01-08       Impact factor: 4.475

10.  Variational Implicit Solvation with Poisson-Boltzmann Theory.

Authors:  Shenggao Zhou; Li-Tien Cheng; Joachim Dzubiella; Bo Li; J Andrew McCammon
Journal:  J Chem Theory Comput       Date:  2014-02-21       Impact factor: 6.006

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