Framework for Quantifying the Efficiency of Competing Signal Transmission Modes in Proteins

On the microscopic level, biological signal transmission relies on coordinated transient structural changes in allosteric proteins that involve sensor and effector modules. The time scales and microscopic details of signal transmission in proteins are often unclear, despite a plethora of structural information on signaling proteins. Based on linear-response theory, we develop the theoretical framework to define frequency-dependent force and displacement transmit functions through proteins and, more generally, viscoelastic media. Transmit functions quantify the fraction of a local time-dependent perturbation at one site, be it a deformation, a force or a combination thereof, that survives at a distant site. They are defined in terms of equilibrium fluctuations from simulations or experimental observations. We apply the framework to our all-atom molecular dynamics simulations of a bacterial histidine kinase protein extensively studied in experiments. For the isolated coiled-coil (CC) motif that connects sensor and effector modules, our analysis reveals that signal propagation through the CC is possible via shift, splay, and twist deformation modes, which is confirmed by simulations of the entire protein. Based on mutation experiments, we infer that the most relevant mode for the biological function of the histidine kinase is the splay deformation. For the β<sub>2</sub>-adrenergic receptor, a transmembrane protein involved in the G-protein signaling pathway, we compare signal transmission across its different structural domains involved in receptor activation.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2026-01-07
DOI
https://doi.org/10.1021/jacs.5c14419
Primary Topic
Bacterial Genetics and Biotechnology
Type
article
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article

Framework for Quantifying the Efficiency of Competing Signal Transmission Modes in Proteins

Anil Kumar Sahoo, Markus S. Miettinen, Hossein Batebi, Roland R. Netz et al.
Journal of the American Chemical Society
Bacterial Genetics and Biotechnology
article

Framework for Quantifying the Efficiency of Competing Signal Transmission Modes in Proteins

Anil Kumar Sahoo, Markus S. Miettinen, Hossein Batebi, Roland R. Netz, Richard Schwarzl
article en

Abstract

On the microscopic level, biological signal transmission relies on coordinated transient structural changes in allosteric proteins that involve sensor and effector modules. The time scales and microscopic details of signal transmission in proteins are often unclear, despite a plethora of structural information on signaling proteins. Based on linear-response theory, we develop the theoretical framework to define frequency-dependent force and displacement transmit functions through proteins and, more generally, viscoelastic media. Transmit functions quantify the fraction of a local time-dependent perturbation at one site, be it a deformation, a force or a combination thereof, that survives at a distant site. They are defined in terms of equilibrium fluctuations from simulations or experimental observations. We apply the framework to our all-atom molecular dynamics simulations of a bacterial histidine kinase protein extensively studied in experiments. For the isolated coiled-coil (CC) motif that connects sensor and effector modules, our analysis reveals that signal propagation through the CC is possible via shift, splay, and twist deformation modes, which is confirmed by simulations of the entire protein. Based on mutation experiments, we infer that the most relevant mode for the biological function of the histidine kinase is the splay deformation. For the β<sub>2</sub>-adrenergic receptor, a transmembrane protein involved in the G-protein signaling pathway, we compare signal transmission across its different structural domains involved in receptor activation.

Journal of the American Chemical SocietyVol. 148(2)
Max Planck Institute of Colloids and Interfaces (DE), Yukhnovskii Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine (UA), Computational Physics (United States) (US), Laboratoire de Physique Théorique de la Matière Condensée (FR), University of Bergen (NO), Indian Institute of Science Bangalore (IN), Freie Universität Berlin (DE)
Openalex Percentile: Top 96%
Bacterial Genetics and Biotechnology
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