Molecular information theory meets protein folding

TL;DR

This paper applies molecular information theory to analyze protein folding, revealing insights into the information content, conformational states, and energy efficiency of the folding process, bridging sequence evolution and energy landscape concepts.

Contribution

It introduces a novel application of molecular information theory to protein folding, quantifying information content and efficiency, and linking sequence data with energetic and structural properties.

Findings

Average information in evolved proteins is ~2.2 bits per site.

Effective amino acid alphabet size is about 5.

Energy-to-information conversion efficiency is around 50%.

Abstract

We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ~2.2 $\pm$ 0.3 bits/(site operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy and the energetics of protein folding.