# Robust and accurate computational estimation of the polarizability tensors of macromolecules

## TL;DR

This paper introduces a regression-based method to efficiently estimate the polarizability tensors of large macromolecules like proteins, bypassing computationally intensive quantum chemistry calculations, with applications in molecular alignment and optical property estimation.

## Contribution

It presents a novel regression model correlating amino acid polarizabilities with perfect conductors, enabling accurate and scalable polarizability tensor predictions for large biomolecules.

## Key findings

- Accurately predicts polarizability tensors of proteins.
- Estimates dielectric constants from regression slopes.
- Benchmarked against quantum chemistry and experimental data.

## Abstract

Alignment of molecules through electric fields minimizes the averaging over orientations, e. g., in single particle imaging experiments. The response of molecules to external ac electric fields is governed by their polarizability tensor, which is usually calculated using quantum-chemistry methods. These methods are not feasible for large molecules. Here, we calculate the polarizability tensor of proteins using a regression model that correlates the polarizabilities of the 20 amino acids with perfect conductors of the same shape. The dielectric constant of the molecules could be estimated from the slope of the regression line based on Clausius Mossotti equation. We benchmark our predictions against the quantum chemistry results for the Trp cage mini protein and the measured dielectric constants of larger proteins. Our method has applications in computing laser-alignment of macromolecules, for instance, benefiting single particle imaging, as well as for the estimation of the optical and electrostatic characteristics of proteins and other macromolecules.