Fluctuation-Driven Synergy, Redundancy, Signal to Noise Ratio and Error Correction in Protein Allostery

Abstract

This study explores the relationship between residue fluctuations and molecular communication in proteins, emphasizing the role of these dynamics in allosteric regulation. We employ computational tools including the Gaussian Network Model, mutual information, and interaction information, to analyze how stochastic interactions among residues contribute to functional interactions while also introducing noise. Our approach is based on the postulate that residues experience continuous stochastic bombardment from impulses generated by their neighbors, forming a complex network characterized by small-world scaling topology. By mapping these interactions through the Kirchhoff matrix framework, we demonstrate how conserved correlations enhance signaling pathways and provide stability against noise-like fluctuations. Notably, we highlight the importance of selecting relevant eigenvalues to optimize the signal-to-noise ratio in our analyses, a topic that has yet to be thoroughly investigated in the context of residue fluctuations. This work underscores the significance of viewing proteins as adaptive information processing systems, and emphasizes the fundamental mechanisms of biological information processing. The basic idea of this paper is the following: Given two interacting residues on an allosteric path, what are the contributions of the remaining residues on this interaction. This naturally leads to the concept of synergy, redundancy and noise in proteins, which we analyze in detail for three proteins CheY, Tyrosine Phosphatase and {beta}-Lactoglobulin.