  Configurational entropy is an important factor in the free energy change of
many macromolecular recognition and binding processes, and has been intensively
studied. Despite great progresses that have been made, the global sampling
remains to be a grand challenge in computational analysis of relevant
processes. Here we propose and demonstrate an entropy estimation method that is
based on physical partition of configurational space and can be readily
combined with currently available methodologies. Tests with two globular
proteins suggest that for flexible macromolecules with large and complex
configurational space, accurate configurational entropy estimation may be
achieved simply by considering the entropically most important subspace. This
conclusion effectively converts an exhaustive sampling problem into a local
sampling one, and defines entropically dominant state for proteins and other
complex macromolecules. The conceptional breakthrough is likely to positively
impact future theoretical analysis, computational algorithm development and
experimental design of diverse chemical and biological molecular systems.
