An evolutionary algorithm to model structural excursions of a protein

Abstract

Excursions of a protein between di erent structures at equilibrium are key to its ability to modulate its biological function. e energy landscape, which organizes structures available to a protein by their energetics, contains all the information needed to characterize and simulate structural excursions. Computational research aims to uncover such excursions to complement wet-laboratory studies in characterizing protein equilibrium dynamics. Popular strategies adapt the robot motion planning framework and construct full or partial, structured representations of the energy landscape. In this paper, we present a novel, complementary approach based on evolutionary computation. We propose an evolutionary algorithm that evolves path representations of a speci c structural excursion without a priori construction of the energy landscape. Preliminary applications on healthy and pathogenic variants of a protein central to human health are promising and warranting further investigation of evolutionary search techniques for modeling protein structural excursions.

Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation

“…Finally, an interesting idea is that our modeling of the dynamic folding process can provide transitions of a protein between different structures at equilibrium, e.g., transitions between healthy and pathogenic variants of proteins involved in diseases. In previous approaches, for example Sapin et al [35], an evolutionary algorithm was used to evolve path representations of structural transitions in proteins. Contrary to this approach of finding a particular transition path between each pair of protein variants at equilibrium, our modeling of the folding process can provide these structural transitions in a more direct way, if the neural-CA model is evolved to provide such transitions through the energy landscape.…”

Using subpopulation EAs to map molecular structure landscapes

“…Work in [7][8][9] additionally debuts decentralized selection operators to retain diversity. Work in [26,29] pursues various recombination strategies to promote generation of diverse candidates, hybridization for better exploitation, and non-local optimization operators to balance between exploration and exploitation.…”