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Genetic Algorithm Optimization of Force Field Parameters: Application to a Coarse-Grained Model of RNA

  • Filip Leonarski
  • Fabio Trovato
  • Valentina Tozzini
  • Joanna Trylska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6623)

Abstract

Determining force field parameters for molecular dynamics simulations of reduced models of biomolecules is a long, troublesome, and exhaustive process that is often performed manually. To improve this parametrization procedure we apply a continuous-space Genetic Algorithm (GA). GA is implemented to optimize parameters of a coarse-grained potential energy function of ribonucleic acid (RNA) molecules. The parameters obtained using GA are correctly reproducing the dynamical behavior of an RNA helix and other RNA tertiary motifs. Therefore, GA can be a useful tool for force field parametrization of the effective potentials in coarse-grained molecular models.

Keywords

Molecular Dynamic Simulation Potential Energy Function Force Field Parameter Genetic Algorithm Optimization Nonbonded Interaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Filip Leonarski
    • 1
    • 2
  • Fabio Trovato
    • 3
  • Valentina Tozzini
    • 3
  • Joanna Trylska
    • 1
  1. 1.Interdisciplinary Centre for Mathematical and Computational ModellingUniversity of WarsawWarsawPoland
  2. 2.Faculty of ChemistryUniversity of WarsawWarsawPoland
  3. 3.NEST CNR-INFMScuola Normale SuperiorePisaItaly

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