Extremal optimization: heuristics via coevolutionary avalanches
Computing in Science and Engineering
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The protein folding problem consists of predicting the functional (native)structure of the protein given its linear sequence of amino acids. Despite extensive progress made in understanding the process of protein folding, this problem still remains extremely challenging. In this paper we introduce, implement and evaluate the Extremal Optimization method - a biologically inspired approach which has been applied very successfully to other optimization problems - for the protein folding problem using a widely studied Gō-model of folding. Standard methods based on the variants of the Monte Carlo method have difficulty exploring low-energy regions efficiently due to the ruggedness of the search landscapes. Most computational methods in the protein folding literature do not keep track of which interactions remain unsatisfied during the search. Instead, in this paper, we propose an adaptive meta-search method which ensures that unexplored promising parts of the search landscape are visited. This is achieved by implementing an adaptive Extremal Optimization meta-search that guides a standard Monte Carlo sampling. We demonstrate that our Extremal Optimization meta-search compares favorably with currently best-performing Replica Exchange Monte Carlo method in reaching the native state for long proteins under the Gō-model potential. Additionally, we show that our novel approach samples larger ensembles of near-native structures by plotting parts of the energy landscape sampled during the search. Furthermore, we find that it scales well with the increasing sequence length. To our best knowledge this is the first application of Extremal Optimization to the protein folding problem.