Genetic algorithm optimization of force field parameters: application to a coarse-grained model of RNA

  • Authors:

  • Filip Leonarski;Fabio Trovato;Valentina Tozzini;Joanna Trylska

  • Affiliations:

  • Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland and Faculty of Chemistry, University of Warsaw, Warsaw, Poland;NEST CNR-INFM, Scuola Normale Superiore, Pisa, Italy;NEST CNR-INFM, Scuola Normale Superiore, Pisa, Italy;Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland

  • Venue:
  • EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
  • Year:
  • 2011

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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 coarsegrained 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.