Unveiling GruPol: Predicting Electric and Electrostatic Properties of Macromolecules via the Building Block Approach

Abstract

Understanding electrostatics and electric properties of macromolecules is crucial in uncovering the intricacies of their behavior and functionality. The precise knowledge of these properties enhances our ability to manipulate and engineer macromolecules for diverse applications, spanning from drug design to materials science. Having that in mind, we present here the GruPol database approach to characterize and accurately predict dipole moments, static polarizabilities, and electrostatic potential of proteins and their subunits. The method involves partitioning of the electron density, calculated at the M06-HF/aug-cc-pVDZ level of theory, of small peptides into predefined building blocks that are averaged over the database. By manipulating and positioning these building blocks, GruPol enables the description of proteins assembled from over nearly 100 residual entries, allowing for efficient and precise computation of the above-mentioned properties across a broad range of proteins. The database enables the user to include solvent effects as well as define protonation states on the protein's backbone to account for pH variations. The precision of the proposed scheme is benchmarked against experimental data for myoglobin species.