Exploring the energy landscape of model proteins: a metric criterion for the determination of dynamical connectivity

TL;DR

This paper introduces a metric-based method to reconstruct and analyze the energy landscape of small peptides, revealing how native configurations are more accessible and stable, with implications for understanding protein folding.

Contribution

It presents a novel metric criterion for identifying directly connected minima in the energy landscape of model proteins, enhancing landscape reconstruction accuracy.

Findings

Native configurations are more accessible and stable.

A funnel-like energy landscape structure is observed.

The method effectively distinguishes connected minima in peptide models.

Abstract

A method to reconstruct the energy landscape of small peptides is presented with reference to a 2d off--lattice model. The starting point is a statistical analysis of the configurational distances between generic minima and directly connected pairs (DCP). As the mutual distance of DCP is typically much smaller than that of generic pairs, a metric criterion can be established to identify the great majority of DCP. Advantages and limits of this approach are thoroughly analyzed for three different heteropolymeric chains. A funnel--like structure of the energy landscape is found in all of the three cases, but the escape rates clearly reveal that the native configuration is more easily accessible (and is significantly more stable) for the sequence that is expected to behave as a real protein.