Deciphering Molecular Charge Anisotropy: the Case of Antibody Solutions

TL;DR

This study develops a multiscale, neural network-assisted framework to understand how heterogeneous charge distributions on proteins influence their solution behavior, aiding in biomolecular design and formulation.

Contribution

It introduces a novel inverse design method linking molecular electrostatics to collective properties in protein solutions, validated on monoclonal antibodies.

Findings

Charge patterns can be tuned to match experimental data

Specific charge arrangements affect solution structure

The approach is transferable to other heterogeneously charged systems

Abstract

Electrostatic interactions fundamentally govern the structure, stability, and dynamics of charged (bio)matter, yet the impact of heterogeneous and anisotropic charge distributions on the behavior of protein solutions remains elusive. Here, we introduce a versatile multiscale framework that directly connects molecular-level electrostatics to collective properties via a colloid-inspired coarse-grained modeling combined with neural network-assisted optimization. Using monoclonal antibodies as model system, our inverse design approach identifies charge patterns capable of reliably reproducing experimental structure factors, osmotic compressibility and collective diffusion coefficients in a wide region of protein concentrations. Close inspection of our data further uncovers how specific physical features and spatial arrangements of localized charge patches significantly influence the solution structure. This transferable strategy provides a predictive pathway to decode and control charge-driven interactions in complex biomolecules and, more generally, in heterogeneously-charged soft matter systems, with immediate relevance to protein formulation and biomaterials engineering.