Information transmission and processing in G-protein-coupled-receptor complexes.

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Authors: Jones, Roger D, Alan M. Jones, Achille Giacometti
Cited by: 0

Abstract

We present a general theoretical framework for molecular computation in biological systems and apply it to G-protein-coupled receptors (GPCRs), which serve as central regulators of cellular information processing. Despite their importance, the physical principles underlying GPCR switching remain incompletely understood. Using nonequilibrium thermodynamics, we construct a model that identifies the parameters governing receptor-state transitions. The framework shows that the configuration of the switch is governed by two factors: the ATP/GTP-driven chemical flux through the receptor complex and the free-energy difference between competing switch states. The model predicts that GPCRs can occupy three quasistable configurations, corresponding to "on," "off," and an intermediate state, each representing a local maximum in information transmission. Switch states can also be characterized by whether the switch supports a net chemical flux. Active states support sustained chemical flux, whereas inactive states do not. The model incorporates reciprocal conformation-fit changes between ligand and receptor. As such, the model predicts that phosphatase activity, represented as an effective energy barrier, primarily determines whether the switch occupies the "on" or "off" state, whereas kinase activity maintains flux without directly setting state occupancy. These predictions based on experimental evidence point to new targets for drug design. Comparison with label-free impedance measurements supports the existence of multiple quasistable states that depend on ligand conformation. Because the framework relies on general nonequilibrium principles rather than system-specific biochemistry, it extends naturally to other biological switching systems driven by chemical flux.

Fields of Study & Subjects

Receptor Mechanisms and Signaling Advanced Thermodynamics and Statistical Mechanics Gene Regulatory Network Analysis

Publication details

Journal / source
Carolina Digital Repository (University of North Carolina at Chapel Hill)
Publication date
29 January 2026