Curvature of the energy landscape and folding of model proteins

TL;DR

This paper investigates the geometric properties of energy landscapes in model proteins, demonstrating that curvature fluctuations can identify folding transitions and distinguish between folding and collapse behaviors without prior native state knowledge.

Contribution

It introduces a geometric approach to analyze energy landscapes, linking curvature fluctuations to folding transitions in off-lattice polymer models.

Findings

Curvature fluctuations mark the folding transition.

Distinguishes protein-like folding from hydrophobic collapse.

Does not require native state information.

Abstract

We study the geometric properties of the energy landscape of coarse-grained, off-lattice models of polymers by endowing the configuration space with a suitable metric, depending on the potential energy function, such that the dynamical trajectories are the geodesics of the metric. Using numerical simulations, we show that the fluctuations of the curvature clearly mark the folding transition, and that this quantity allows to distinguish between polymers having a protein-like behavior (i.e., that fold to a unique configuration) and polymers which undergo a hydrophobic collapse but do not have a folding transition. These geometrical properties are defined by the potential energy without requiring any prior knowledge of the native configuration.