Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs

The problem of finding an optimal positioning for the side chain residues of a protein is called the side chain placement or side chain prediction problem. It can be posed as an optimization problem in the discrete domain. In this paper we use an estimation of distribution algorithm to address this optimization problem. Using a set of 50 difficult protein instances, it is shown that the addition of dependencies between the variables in the probabilistic model can improve the quality of the solutions achieved for most of the instances considered. However, we also show that only when information about the known interactions between the residues is considered in the creation of the probabilistic model, the addition of the dependencies contributes to improve the quality of the solutions obtained.

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