It is widely established that understanding protein dynamics is essential to bridging structure and function. One major challenge in computational modeling of protein dynamics is the computational cost and time required for viewing events of biological significance, the time scale and cooperative nature of which is often beyond the reach of conventional molecular dynamics simulations. Among coarse-grained models that have been developed for addressing this issue, elastic network models found wide usage in molecular biophysics and structural biology. The global modes of motion predicted by ENMs have proven in numerous applications in the last two decades to provide good agreement with experimental data and explain allosteric behavior, despite the simplicity of the model and the lack of specificity. Application to supramolecular structures has been a major utility of these models. More recently, ENMs proved useful to exploring chromosomal dynamics, using data from Hi-C experiments to reconstruct in silico the connectivity of the chromatin. Another topic finding increasingly wider applications is the mapping of the protein-drug interaction space by adopting a bipartite network representation. We will present the foundations of the ENM theory and methods, and new insights gained from the applications to chromosomal dynamics [1]. We will further describe recent progress in mapping drugs, proteins and pathways, building on a network-based quantitative systems pharmacology method. Finally, we will show how machine learning algorithms that incorporate ENM predictions provide an improved assessment of the effect of mutations on function, compared to those based on sequence and structure exclusively [2].
1. Sauerwald N, Zhang S, Kingsford C, Bahar I. (2017) Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings Nucleic Acids Res45:3663-3673
2. Ponzoni L, Bahar I. (2018) Structural dynamics is a determinant of the functional significance of missense variants. Proc Natl Acad Sci USA 115: 4164-4169