Dihedral-angle Gaussian distribution driving protein folding

TL;DR

This paper introduces a simple Gaussian-based angular random walk model to simulate protein folding, capturing key structural properties and highlighting the importance of narrow angular distributions over pure randomness.

Contribution

The paper presents a novel angular random walk model using Gaussian distributions to simulate protein structures, emphasizing the role of narrow dihedral angle distributions.

Findings

Proteins with alpha-helix motifs are more compact.

The model effectively captures structural properties of proteins.

Randomness alone does not explain protein folding, narrow distributions are crucial.

Abstract

The proposal of this paper is to provide a simple angular random walk model to build up polypeptide structures, which encompass properties of dihedral angles of folded proteins. From this model, structures will be built with lengths ranging from 125 up to 400 amino acids for the different fractions of secondary structure motifs, which dihedral angles were randomly chosen according to narrow Gaussian probability distributions. In order to measure the fractal dimension of proteins three different cases were analyzed. The first contained alpha-helix structures only, the second beta-strands structures and the third a mix of alpha-helices and beta-sheets. The behavior of proteins with alpha-helix motifs are more compacted than in other situations. The findings herein indicate that this model describes some structural properties of a protein and suggest that randomness is an essential ingredient but proteins are driven by narrow angular Gaussian probability distributions and not by random-walk processes.