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• W2552685162 abstract "Stochastic search is often the only viable option to address complex optimization problems. Recently, evolutionary algorithms have been shown to handle challenging continuous optimization problems related to protein structure modeling. Building on recent work in our laboratories, we propose an evolutionary algorithm for efficiently mapping the multi-basin energy landscapes of dynamic proteins that switch between thermodynamically stable or semi-stable structural states to regulate their biological activity in the cell. The proposed algorithm balances computational resources between exploration and exploitation of the nonlinear, multimodal landscapes that characterize multi-state proteins via a novel combination of global and local search to generate a dynamically-updated, information-rich map of a protein's energy landscape. This new mapping-oriented EA is applied to several dynamic proteins and their disease-implicated variants to illustrate its ability to map complex energy landscapes in a computationally feasible manner. We further show that, given the availability of such maps, comparison between the maps of wildtype and variants of a protein allows for the formulation of a structural and thermodynamic basis for the impact of sequence mutations on dysfunction that may prove useful in guiding further wet-laboratory investigations of dysfunction and molecular interventions." @default.
• W2552685162 created "2016-11-30" @default.
• W2552685162 creator A5007980694 @default.
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• W2552685162 date "2018-05-01" @default.
• W2552685162 modified "2023-10-11" @default.
• W2552685162 title "From Optimization to Mapping: An Evolutionary Algorithm for Protein Energy Landscapes" @default.
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• W2552685162 doi "https://doi.org/10.1109/tcbb.2016.2628745" @default.
• W2552685162 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28113951" @default.
• W2552685162 hasPublicationYear "2018" @default.
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