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Hydrogen bonds are weak attractions between atoms that have formed other chemical bonds. One of these atoms is electronegative, like oxygen, and has a partial negative charge. The other is a hydrogen atom that has bonded with another electronegative atom and has a partial positive charge.
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A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
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DNET: A Graph-Based Tool and Workflow for Dynamic Hydrogen-Bond Networks and Applications for Visual Rhodopsins.

Éva Bertalan1, Matthew J Rodrigues2,3, Deborah Walter2

  • 1Physikzentrum, RWTH-Aachen University, D-52074 Aachen, Germany.

Journal of Chemical Theory and Computation
|January 6, 2026
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Summary
This summary is machine-generated.

A new tool, DNET, analyzes dynamic protein-water hydrogen-bond networks in G Protein-Coupled Receptors (GPCRs). It reveals complex H-bond dynamics and pKa fluctuations, showing mutations alter these networks in visual rhodopsin.

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Area of Science:

  • Biochemistry and structural biology
  • Computational biology and bioinformatics
  • Molecular biophysics

Background:

  • G Protein-Coupled Receptors (GPCRs) are crucial for cellular signaling and are key drug targets.
  • GPCR activation involves structural changes transmitted through transmembrane domains, influenced by dynamic hydrogen-bond networks.
  • Understanding these networks is vital for elucidating GPCR function and drug design.

Purpose of the Study:

  • Introduce DNET, a novel graph-based computational tool for analyzing dynamic protein-water hydrogen-bond networks.
  • Characterize the H-bond network dynamics and pKa fluctuations in jumping spider rhodopsin 1 (JSR-1).
  • Investigate the impact of mutations on the JSR-1 H-bond network and its functional implications.

Main Methods:

  • Developed DNET, a portable Python tool for processing simulation trajectories and computing dynamic H-bond networks.
  • Integrated DNET with PROPKA to analyze pKa fluctuations within H-bond networks.
  • Applied DNET to study wild-type and mutated JSR-1 proteins, analyzing UV-vis spectroscopy and H-bond dynamics.

Main Results:

  • DNET efficiently computes dynamic protein-water H-bond networks and provides detailed residue-level analyses.
  • JSR-1 exhibits complex retinal H-bond network dynamics with single and multiple conformational modes.
  • Mutations in JSR-1 alter the electrostatic environment of the retinal Schiff base and its H-bond network.

Conclusions:

  • DNET is a valuable tool for dissecting complex H-bond dynamics in GPCRs.
  • The study reveals intricate H-bond network dynamics and associated pKa fluctuations in a visual rhodopsin.
  • Mutations significantly impact the H-bond network, offering insights into rhodopsin function and potential therapeutic strategies.