Fluctuation matching approach for elastic network model and structure-based model of biomacromolecules

Abstract

Elastic network models (ENMs) based on simple harmonic potential energy function have been proven over the last decade to be reliable computational models for understanding the intrinsic dynamics of biomacromolecules. In the original ENMs, the spring constants for different contact pairs are assumed to be identical, while there are a number of recent developments to determine non-uniform spring constants from atomistic force fields or experimental information. In particular, the fluctuation matching approaches in coarse-grained modeling can be applied to build more realistic heterogeneous ENMs, using information from an atomistic force field or experimental B-factors. The same type of approaches is further implemented to parameterize heterogeneous structure-based models, which can be considered as a natural extension of ENMs in terms of the potential energy function. In this review, we give an overview of different fluctuation matching methods adopted for ENMs and structure-based models, including an improved formulation and algorithm based on the relative entropy scheme.