Deciphering Molecular Charge Anisotropy: The Case of Antibody Solutions

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 a 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. By further inspecting our data, we find specific physical features and spatial arrangements of localized charge patches that significantly influence the solution structure, uncovering clear design principles. The strategy we develop provides a transferable pathway to decode charge-driven interactions in complex biomolecules and, more generally, in heterogeneously charged soft-matter systems, with relevance to protein formulation and biomaterials engineering.