Ivet Bahar (University of Pittsburgh)

Structure-Encoded Dynamics of Proteins: Learning from Network Models and Experiments

Comparison of computational and experimental data in recent years highlights
the significance of the intrinsic dynamics of proteins in facilitating, or
enabling, biological functions. Intrinsic dynamics refer to the collective
motions encoded in the native structure, predominantly defined by the network
of inter-residue interactions. Elastic network models (ENMs) coupled with
normal mode analyses have proven in recent years to serve as useful
computational tools for elucidating such structure-encoded propensities. In
particular, the soft modes predicted by ENMs disclose cooperative mechanisms of
reconfiguration, which relate to structural changes undergone during allosteric
responses or molecular machinery. We present here in silico approaches based on
ENMs of various levels of granularity, along with several examples of their use
in unraveling biomolecular mechanisms of function, including their application
to supramolecular systems dynamics. We also highlight the significance of
complementing ENM-based studies by sequence-based correlation analyses and
structure-based hybrid methods that bridge between atomic and coarse-grained
scales.