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.
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Leonarski, F., Trovato, F., Tozzini, V., Trylska, J. (2011). Genetic Algorithm Optimization of Force Field Parameters: Application to a Coarse-Grained Model of RNA. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_15
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DOI: https://doi.org/10.1007/978-3-642-20389-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20388-6
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