  The proper biological functioning of proteins often relies on the occurrence
of coordinated fluctuations around their native structure, or of wider and
sometimes highly elaborated motions. Coarse-grained elastic-network
descriptions are known to capture essential aspects of conformational dynamics
in proteins, but have so far remained mostly phenomenological, and unable to
account for the chemical specificities of amino acids. Here, we propose a
method to derive residue- and distance-specific effective harmonic potentials
from the statistical analysis of an extensive dataset of NMR conformational
ensembles. These potentials constitute dynamical counterparts to the mean-force
statistical potentials commonly used for static analyses of protein structures.
In the context of the elastic network model, they yield a strongly improved
description of the cooperative aspects of residue motions, and give the
opportunity to systematically explore the influence of sequence details on
protein dynamics.
