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Path-based Guidance of an Evolutionary Algorithm in Mapping a Fitness Landscape and its Connectivity

Understanding function regulation in proteins that switch between different structural states at equilibrium requires both finding the basins that correspond to such states and computing the sequence of intermediate structures employed (i.e., the path taken) in basin-to-basin switching. Recent worksuggests that evolutionary strategies can be used to map protein energy landscapes effectively. Further work has shown that the constructed maps can be additionally equipped with connectivity information to help identify basin-switching paths. Here we highlight a potential issue when the problems of mapping and path finding are considered separately. We conduct a simple, proof-of principle study that demonstrates the ability of an EA to allow extracting better paths from an EA-built map when the EA is supplied with the right information. The study is conducted on two key, multi-state proteins of importance to human biology and disease. The results presented here suggest that further research efforts to guide an EA with path-based information are warranted and feasible.