A multiscale hybrid evolutionary algorithm to obtain sample-based representations of multi-basin protein energy landscapes

TLDR: The proposed algorithm makes the first steps to answering the question of how sequence mutations affect function in proteins at the center of proteinopathies by providing the energy landscape as the intermediate explanatory link between protein sequence and function.

Abstract: The emerging picture of proteins as dynamic systems switching between structures to modulate function demands a comprehensive structural characterization only possible through an energy landscape treatment. Only sample-based representations of a protein energy landscape are viable in silico, and sampling-based exploration algorithms have to address the fundamental but challenging issue of balancing between exploration (broad view) and exploitation (going deep). We propose here a novel algorithm that achieves this balance by combining concepts from evolutionary computation and protein modeling research. The algorithm draws samples from a reduced space obtained via principal component analysis of known experimental structures. Samples are lifted from the reduced to an all-atom structure space where they are then mapped to nearby local minima in the all-atom energy landscape. From an algorithmic point of view, this paper makes several contributions, including the design of a local selection operator that is crucial to avoiding premature convergence. From an application point of view, this paper demonstrates the utility of the proposed evolutionary algorithm to advance understanding of multi-basin proteins. In particular, the proposed algorithm makes the first steps to answering the question of how sequence mutations affect function in proteins at the center of proteinopathies by providing the energy landscape as the intermediate explanatory link between protein sequence and function.