Elastic Network Models For Bridging Biomolecular Structure and Function: From Proteins to the Chromatin

Network-based models and methods originating from a broad range of disciplines, from macromolecular statistical mechanics to machine learning, have found wide applications in computational biology. In particular, elastic network models (ENMs) of polymer physics, have proven to be useful tools for investigating the collective dynamics of biomolecular systems and making inferences on their mechanism of function. The major strengths of these approaches are their conceptual and computational simplicity, and their ability to provide a global view of cooperative dynamics, albeit at low resolution. There is now increasing evidence in support of the functional significance of spatial connectivity (as described by ENMs) between interaction sites at multiple levels, from single residues on proteins to gene loci on the chromatin. We will provide an overview of the state-of-the-art regarding the advances in modeling and investigating the mechanism of action of biomolecules using ENMs, as well as recent extensions to predicting the effect of mutations on function,¹ or gene-gene correlations at the chromatin level.²

1. Ponzoni L, Bahar I. (2018) Structural dynamics is a determinant of the functional significance of missense variants. Proc Natl Acad Sci USA DOI:10.1073/pnas.1715896115
2. Sauerwald N, Zhang S, Kingsford C, Bahar I. (2017) Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplingsNucleic Acids Res 45:3663-3673