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Dielectric response in proteins: The proteotronics approach

E. Alfinito, M. Beccaria

TL;DR

This work proposes a user-friendly method to calculate the relative permittivity that can be readily integrated into proteotronics workflows and compares the results with those obtained using a classical macroscopic method.

Abstract

The dielectric properties of proteins, particularly in their hydrated state, have been extensively studied. Numerous theoretical and experimental investigations have reported values of both the permittivity and the intrinsic dipole moments of specific proteins under well-defined hydration conditions. Since even approximate estimates of these properties are relevant from both fundamental and applied perspectives, we propose a user-friendly method to calculate the relative permittivity that can be readily integrated into proteotronics workflows. To validate the proposed approach, we compare the results with those obtained using a classical macroscopic method. The outcomes are consistent and contribute further insight into this long-debated issue.

Dielectric response in proteins: The proteotronics approach

TL;DR

This work proposes a user-friendly method to calculate the relative permittivity that can be readily integrated into proteotronics workflows and compares the results with those obtained using a classical macroscopic method.

Abstract

The dielectric properties of proteins, particularly in their hydrated state, have been extensively studied. Numerous theoretical and experimental investigations have reported values of both the permittivity and the intrinsic dipole moments of specific proteins under well-defined hydration conditions. Since even approximate estimates of these properties are relevant from both fundamental and applied perspectives, we propose a user-friendly method to calculate the relative permittivity that can be readily integrated into proteotronics workflows. To validate the proposed approach, we compare the results with those obtained using a classical macroscopic method. The outcomes are consistent and contribute further insight into this long-debated issue.
Paper Structure (2 sections, 13 equations, 3 figures, 2 tables)

This paper contains 2 sections, 13 equations, 3 figures, 2 tables.

Table of Contents

  1. Model function
  2. Tables

Figures (3)

  • Figure 1: Normalized effective permittivity, $k$, for the selected dataset. The calculation is performed using three test functions (linear, sigmoidal, and exponential). Proteins with different geometrical characteristics (approximately spherical and elongated) are indicated by distinct symbols (dots and diamonds, respectively). Solid lines serve as guides to the eye. Dashed lines denote the mean values for spherical (lower) and elongated (upper) proteins. The complete protein dataset is listed in Table \ref{['tab:table1']}.
  • Figure 2: Normalized macroscopic susceptibility for the selected dataset: role of the solvent. The normalized permittivity values obtained using Eq. \ref{['eq:eq6dipole']} (the $p - p_0$ model, cyan solid line) and Eq. \ref{['eq:eq9dipole']} (the $p - p$ model, magenta line) are compared with those calculated using the linear test function (black line).
  • Figure 3: Normalized macroscopic susceptibility for the selected dataset: role of volume. The normalized permittivity values obtained using three different power-law scalings ($\chi \propto \Omega^{-n}$, with $n = 0$, $0.5$, and $1$) are compared with those calculated using the linear test function (black line); see also the main text.