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Multi-Objective Optimization Techniques for Conformational Sampling in Template-Free Protein Structure Prediction

Template-free protein structure prediction continues to be a challenging problem in computational biology. State-of-the-art protocols are Monte Carlobased and pay special emphasis on the set of moves and energy guidance. We report here on a complementary platform for decoy sampling that makes use of evolutionary search strategies. We propose that Evolutionary Algorithms (EAs) are effective platforms for the structure prediction problem as an optimization problem, outperforming the Monte Carlo-based sampling of protein conformational space. Moreover, these platforms allow casting the problem as a multi-objective optimization one to deal with the known imperfections in protein energy functions. We compare here different EAs to decoy sampling in the popular Rosetta protocol and show that multi-objective EAs have higher exploration capability and warrant further investigation.