Self-Organized Criticality: A Magic Wand for Protein Physics

TL;DR

This paper proposes that self-organized criticality (SOC) can connect amino acid sequence similarities to structural features, enabling better understanding of protein interactions and evolution, especially in water-membrane proteins like rhodopsin.

Contribution

It introduces a SOC-based method that links short-range amino acid similarities to long-range structural features, improving analysis of protein evolution and interactions.

Findings

Achieves 96% success in correlating sequence properties across species.

Utilizes a hydropathic amino acid metric based on 5526 protein segments.

Simplifies complex protein sequence-structure-function problems.

Abstract

Self-organized criticality (SOC) is a popular concept that has been the subject of more than 3000 articles in the last 25 years. Here we show that SOC may enable theory to connect standard Web-based (BLAST) short-range amino acid (aa) similarities to long-range aa roughening form factors that accurately describe evolutionary trends in water-membrane protein interactions. Our method utilizes a hydropathic aa metric based on 5526 protein segments and thereby encapsulates differential geometrical features of the Protein Data Bank. It easily organizes small aa sequence differences between humans and proximate species. For rhodopsin, the most studied transmembrane signaling protein associated with night vision, it shows that short- and long-range aa sequence properties correlate with 96% success for humans, monkeys, cats, mice and rabbits. Proper application of SOC promises unprecedented simplifications of exponentially complex protein sequence-structure-function problems, both conceptual and practical.