AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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An Evolutionary Search Algorithm to Guide Stochastic Search for Near-Native Protein Conformations with Multiobjective Analysis
Brian Olson, Amarda Shehu

Last modified: 2013-06-29

Abstract


Predicting native conformations of a protein sequence is known as de novo structure prediction and is a central challenge in computational biology. Most computational protocols employ Monte Carlo sampling. Evolutionary search algorithms have also been proposed to enhance sampling of near-native conformations. These approaches bias stochastic search by an energy function, even though current energy functions are known to be inaccurate and drive sampling to non-native energy minima. This paper proposes a multiobjective approach which employs Pareto dominance, rather than total energy, to evaluate a conformation. This multiobjective approach accounts for the fact that terms in an energy function are conflicting optimization criteria. Our analysis is conducted on a diverse set of 20 proteins. Results show that employing Pareto dominance, rather than total energy, to guide stochastic search is more effective at sampling conformations which are both lower in energy and near the protein native structure.

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