Protein-based therapeutics typically require high concentrations of the active protein, which can lead to phase separation (aggregation) and high viscosity. Such solution behaviors can limit the stability, bioavailability, and manufacturability of protein-based therapeutics and reflect the colloidal properties of a given protein. The colloidal properties of a protein are directly influenced by its charge; charge-charge repulsion weakens protein-protein interaction, maintaining protein solubility while dipole moments from asymmetric charge distributions can promote aggregation. Protein charge is a system property affected by its environment, including the solvent composition, pH, and temperature. Thus, the charge calculated by summing the charges of each residue in a protein, as is commonly done in computational methods, may significantly differ from the effective charge of the protein as these calculations do not account for contributions from bound ions. Here, we present an extension of the structure-based approach termed Site Identification by Ligand Competitive Saturation-Biologics (SILCS-Biologics) to predict the effective charge of proteins. The SILCS-Biologics approach was applied on a range of protein targets in different salt environments for which membrane-confined electrophoresis determined charges were previously reported. In this approach, precomputed 3D SILCS FragMaps, encoding protein-functional group interaction patterns, are used to map the binding sites of ions on the target protein's surface and calculate SILCS-Biologics metrics, including the distribution and predicted occupancy of ions bound to the protein surface. These metrics are subsequently processed such that the concentrations of the ions and the presence of excipients or buffers are accounted for to calculate the effective charge of the target protein. Additionally, 3D structures of the ion-bound proteins facilitate the characterization of protein dipole moments and surface charge distribution in different environments. Our studies demonstrate the capacity of the SILCS-Biologics approach to predict the charge of the studied proteins and its potential in uncovering protein-ion interactions and their contributions to protein solubility and function.

Extension of the site-identification by ligand competitive saturation (SILCS)-biologics approach for structure-based protein charge prediction

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