Entropically Dominant State of Proteins

TL;DR

This paper introduces a new entropy estimation method for proteins that focuses on the most entropically significant subspace, simplifying the sampling process and aiding in understanding macromolecular behavior.

Contribution

The study presents a novel entropy estimation approach based on physical partitioning of configurational space, enabling accurate entropy calculation by focusing on the entropically dominant state.

Findings

Accurate entropy estimation achieved by considering the most important subspace.

Converts exhaustive sampling into a local sampling problem.

Defines the entropically dominant state for proteins and complex molecules.

Abstract

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.