Key interaction patterns in proteins revealed by cluster expansion of the partition function

Area of Science:

• Protein structure and biophysics
• Computational biology
• Bioinformatics

Background:

• Protein native conformation is maintained by intricate interaction networks.
• Understanding these networks is key to protein sequence design and fold evolution.

Purpose of the Study:

• To identify and rank elementary interaction patterns critical for protein sequence design.
• To assess the contribution of these patterns to protein fold evolutionary designability.

Main Methods:

• Utilized a cluster expansion of the partition function in sequence space.
• Numerically evaluated the statistical importance and sequence entropy of interaction clusters.
• Applied analysis to a dense finite system.

Main Results:

• Even-numbered cycles were identified as the most significant patterns contributing to the partition function.
• Cliques were generally found to be detrimental to protein stability and design.
• Sequence entropy, a measure of evolutionary designability, varied significantly with different interaction patterns.

Conclusions:

• The study highlights the importance of specific interaction patterns, particularly even-numbered cycles, in protein sequence design.
• Findings provide insights into the evolutionary pressures shaping protein folds and their designability.
• The methodology offers a novel approach to analyzing complex biological networks.