ArticleJournal of computational chemistry2013
Multiscale geometric modeling of macromolecules II: Lagrangian representation.
Article in Journal of computational chemistry, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis.Scientific reports · 2020Article
- A review of mathematical representations of biomolecular data.Physical chemistry chemical physics : PCCP · 2020Review
- DG-GL: Differential geometry-based geometric learning of molecular datasets.International journal for numerical methods in biomedical engineering · 2019Article
- Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.PLoS computational biology · 2018Article
- Multiscale method for modeling binding phenomena involving large objects: application to kinesin motor domains motion along microtubules.Scientific reports · 2016Article
- Object-oriented Persistent Homology.Journal of computational physics · 2016Article
- Multidimensional persistence in biomolecular data.Journal of computational chemistry · 2015Article
- Persistent homology analysis of protein structure, flexibility, and folding.International journal for numerical methods in biomedical engineering · 2014Article
- Variational Implicit Solvation with Poisson-Boltzmann Theory.Journal of chemical theory and computation · 2014Article
- Multiscale geometric modeling of macromolecules I: Cartesian representation.Journal of computational physics · 2014Article
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Authors and funding
5 authors.
Funding
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.
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