Evidence map›Paper›PMID 21279359›Full record

ArticleJournal of mathematical biology2011

Differential geometry based solvation model II: Lagrangian formulation.

Zhan Chen, Nathan A Baker, G W Wei

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

  1. Article
  2. Article
  3. EVOLUTIONARY DE RHAM-HODGE METHOD.Discrete and continuous dynamical systems. Series B · 2021
    Article
  4. A review of mathematical representations of biomolecular data.Physical chemistry chemical physics : PCCP · 2020
    Review
  5. DG-GL: Differential geometry-based geometric learning of molecular datasets.International journal for numerical methods in biomedical engineering · 2019
    Article
  6. Article
  7. Improvements to the APBS biomolecular solvation software suite.Protein science : a publication of the Protein Society · 2018
    Article
  8. Object-oriented Persistent Homology.Journal of computational physics · 2016
    Article
  9. Review
  10. Article
  11. Multidimensional persistence in biomolecular data.Journal of computational chemistry · 2015
    Article
  12. Problems of robustness in Poisson-Boltzmann binding free energies.Journal of chemical theory and computation · 2015
    Article
  13. Persistent homology analysis of protein structure, flexibility, and folding.International journal for numerical methods in biomedical engineering · 2014
    Article
  14. Variational Implicit Solvation with Poisson-Boltzmann Theory.Journal of chemical theory and computation · 2014
    Article
  15. Article
  16. Article
  17. Multiscale Multiphysics and Multidomain Models I: Basic Theory.Journal of theoretical & computational chemistry · 2013
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhan ChenDepartment of Mathematics, Michigan State University, Lansing, MI 48824, USA.
Nathan A Baker
G W Wei

Funding

APBS: NANOSCALE BIOMOLECULAR ELECTROSTATICS SOFTWARER01GM069702 · NIGMS · WASHINGTON UNIVERSITY · PI BAKER, NATHAN A. · 2004 to 2021
$6.2M
Collaborative research: Geometric flow approach to implicit solvation modelingR01GM090208 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WEI, GUOWEI · 2009 to 2014
$1.3M
Temperature-Modulated Bioluminescence TomographyR21CA127189 · NCI · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI WANG, GE · 2007 to 2008
$319k
NCI NIH HHS CA127189NCI NIH HHS R21 CA127189NIGMS NIH HHS GM069702NIGMS NIH HHS GM090208NIGMS NIH HHS R01 GM069702NIGMS NIH HHS R01 GM090208
6 · The paper itself

Abstract

Solvation is an elementary process in nature and is of paramount importance to more sophisticated chemical, biological and biomolecular processes. The understanding of solvation is an essential prerequisite for the quantitative description and analysis of biomolecular systems. This work presents a Lagrangian formulation of our differential geometry based solvation models. The Lagrangian representation of biomolecular surfaces has a few utilities/advantages. First, it provides an essential basis for biomolecular visualization, surface electrostatic potential map and visual perception of biomolecules. Additionally, it is consistent with the conventional setting of implicit solvent theories and thus, many existing theoretical algorithms and computational software packages can be directly employed. Finally, the Lagrangian representation does not need to resort to artificially enlarged van der Waals radii as often required by the Eulerian representation in solvation analysis. The main goal of the present work is to analyze the connection, similarity and difference between the Eulerian and Lagrangian formalisms of the solvation model. Such analysis is important to the understanding of the differential geometry based solvation model. The present model extends the scaled particle theory of nonpolar solvation model with a solvent-solute interaction potential. The nonpolar solvation model is completed with a Poisson-Boltzmann (PB) theory based polar solvation model. The differential geometry theory of surfaces is employed to provide a natural description of solvent-solute interfaces. The optimization of the total free energy functional, which encompasses the polar and nonpolar contributions, leads to coupled potential driven geometric flow and PB equations. Due to the development of singularities and nonsmooth manifolds in the Lagrangian representation, the resulting potential-driven geometric flow equation is embedded into the Eulerian representation for the purpose of computation, thanks to the equivalence of the Laplace-Beltrami operator in the two representations. The coupled partial differential equations (PDEs) are solved with an iterative procedure to reach a steady state, which delivers desired solvent-solute interface and electrostatic potential for problems of interest. These quantities are utilized to evaluate the solvation free energies and protein-protein binding affinities. A number of computational methods and algorithms are described for the interconversion of Lagrangian and Eulerian representations, and for the solution of the coupled PDE system. The proposed approaches have been extensively validated. We also verify that the mean curvature flow indeed gives rise to the minimal molecular surface and the proposed variational procedure indeed offers minimal total free energy. Solvation analysis and applications are considered for a set of 17 small compounds and a set of 23 proteins. The salt effect on protein-protein binding affinity is investigated with two protein complexes by using the present model. Numerical results are compared to the experimental measurements and to those obtained by using other theoretical methods in the literature.

Indexed as

Models, ChemicalAlgorithmsComputer SimulationModels, MolecularOrganic ChemicalsProtein BindingProteinsSaltsSolubilitySolutionsSolventsStatic ElectricitySurface PropertiesThermodynamicsOrganic ChemicalsProteinsSaltsSolutionsSolvents

Identifiers

PMID21279359
PMCPMC3113640

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.