Modulating non-native aggregation and electrostatic protein–protein interactions with computationally designed single-point mutations

Abstract

Non-native protein aggregation is a ubiquitous challenge in the production, storage and administration of protein-based biotherapeutics. This study focuses on altering electrostatic protein-protein interactions as a strategy to modulate aggregation propensity in terms of temperature-dependent aggregation rates, using single-charge variants of human γ-D crystallin. Molecular models were combined to predict amino acid substitutions that would modulate protein-protein interactions with minimal effects on conformational stability. Experimental protein-protein interactions were quantified by the Kirkwood-Buff integrals (G22) from laser scattering, and G22 showed semi-quantitative agreement with model predictions. Experimental initial-rates for aggregation showed that increased (decreased) repulsive interactions led to significantly increased (decreased) aggregation resistance, even based solely on single-point mutations. However, in the case of a particular amino acid (E17), the aggregation mechanism was altered by substitution with R or K, and this greatly mitigated improvements in aggregation resistance. The results illustrate that predictions based on native protein-protein interactions can provide a useful design target for engineering aggregation resistance; however, this approach needs to be balanced with consideration of how mutations can impact aggregation mechanisms.

Biophysical characterization and molecular simulation of electrostatically driven self‐association of a single‐chain antibody

“…Preliminary calculations of B 22 values for the scFv (with the polypeptide linker omitted from the structure) using molecular simulations (see methods) did not show electrostatic attractions under any pH or ionic strength. This was despite good agreement between the CG modeling and experimental results for other proteins . One hypothesis for this lack of qualitative agreement between experimental and simulation results is that the linker may influence protein–protein interactions since it contains several residues with charged side‐chains, despite the linker being net neutral.…”

“…Protein–protein interactions (PPI) control a range of properties for protein solutions, and are due to a combination of long‐range electrostatic interactions, and short‐range interactions such as hydrophobic attractions, steric repulsions, and van der Waals attractions . Protein–protein interactions are influenced by the sequence and structure of protein molecules, the solution conditions (such as pH, ionic strength), and other environmental conditions (such as temperature and pressure) . PPI include both the “weak” or non‐specific interactions that can influence physical properties such as solubility and solution viscosity, and “strong” or highly specific interactions such as those between a receptor and its ligand, or an antibody and its antigen .…”

“…PPI may also influence protein solubility and the viscosity of protein solutions . Recent work has focused on optimization of protein solution properties using formulation and/or protein engineering approaches that target protein–protein interactions …”

“…Coarse‐grained molecular modeling approaches are of interest because they may provide insight into the physics of inter‐protein interactions without the substantial computational time and resources that an all‐atom structural description and explicit solvent would require . Coarse‐grained modeling has focused on, for example, calculating B 22 as a function of pH and ionic strength and predicting amino‐acid substitutions to increase intermolecular repulsions between protein molecules …”

“…[1][2][3] Protein-protein interactions are influenced by the sequence and structure of protein molecules, the solution conditions (such as pH, ionic strength), and other environmental conditions (such as temperature and pressure). 1,2,[4][5][6] PPI include both the "weak" or non-specific interactions that can influence physical properties such as solubility and solution viscosity, [6][7][8] and "strong" or highly specific interactions such as those between a receptor and its ligand, or an antibody and its antigen. 9,10 The latter usually result in an effective binding event that has a measurable equilibrium dissociation constant, K d , that lies in the (sub)micromolar range.…”