Living systems must constantly acquire, analyze, and adapt to information in their environments in order to survive. Allostery plays an essential role in this process by thermodynamically coupling the system’s multitude of biomolecular processes in a manner that is coordinated, robust, and adaptive. For example, we have recently shown that allostery in sodium-coupled symporters drives forward symport while inhibiting other forms of unproductive transport. However, the study of allostery has been largely divided into i) physical models utilizing severe assumptions, and ii) statistical models that have limited predictive power. We present a new statistical mechanical approach to allostery that bridges physical and statistical models and can applied generally to any allosteric system.  We show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function, which we call the “allostery landscape” that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the φ/ψ free energy surface, and by identifying the interactions that are necessary for this coupling.<br/>

Mind the gap: Building a bridge between physical and statistical models of allostery

253rd National Meeting (2017)

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