Application of a Simple Short-Range Attraction and Long-Range Repulsion Colloidal Model toward Predicting the Viscosity of Protein Solutions

Abstract

Some hard-sphere colloidal models have been criticized for inaccurately predicting the solution viscosity of complex biological molecules like proteins. Competing short-range attractions and long-range repulsions, also known as short-range attraction and long-range repulsion (SALR) interactions, have been thought to affect the microstructure of a protein solution at low to moderate ionic strength. However, such interactions have been implicated primarily in causing phase transition, protein gelation, or reversible cluster formation, and their effect on protein solution viscosity change is not fully understood. In this work, we show the application of a hard-sphere colloidal model with SALR interactions toward predicting the viscosity of dilute to semi-dilute protein solutions. The comparison is performed for a globular-shaped albumin and Y-shaped therapeutic monoclonal antibody that are not explained by previous colloidal models. The model predictions show that it is the coupling between attractions and repulsions that gives rise to the observed experimental trends in solution viscosity as a function of pH, concentration, and ionic strength. The parameters of the model are obtained from measurements of the second virial coefficient and net surface charge/zeta-potential, without additional fitting of the viscosity.

Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations

“…Molecular simulations have been used to investigate the structure and dynamics of the crowded cellular cytoplasm, using molecular-level Brownian dynamics (BD) simulations with implicit solvation models, , coarse-grained (CG) models, and also at the fully atomistic level , (see Ostrowska et al for a recent review). All-atom and coarse-grained MD simulations have also been used to characterize biomolecular condensates, as, for example, formed by liquid–liquid phase separation. − Simulations of dense antibody solutions are typically performed with colloidal hard-sphere models − or with super-CG models in which each individual domain of the mAb is represented by a single CG bead, resulting in the representation of the entire mAb by only a dozen CG beads. − Such models are computationally cheap and thus allow one to simulate large systems with a large copy number of mAbs in the simulation box and to screen a range of different conditions in the simulations such as mAb concentration, ionic strength of the solution, etc. However, the approximations inherent to the CG models can limit their accuracy and typically require extensive parametrization and calibration against experiments and/or higher-level computations, thus hampering the predictive capacity of such CG models.…”

“…All-atom and coarse-grained MD simulations have also been used to characterize biomolecular condensates, as, for example, formed by liquid–liquid phase separation. 20 − 23 Simulations of dense antibody solutions are typically performed with colloidal hard-sphere models 24 − 26 or with super-CG models in which each individual domain of the mAb is represented by a single CG bead, resulting in the representation of the entire mAb by only a dozen CG beads. 27 − 30 Such models are computationally cheap and thus allow one to simulate large systems with a large copy number of mAbs in the simulation box and to screen a range of different conditions in the simulations such as mAb concentration, ionic strength of the solution, etc.…”

Magnetic field enabled in situ control over the structure and dynamics of colloids interacting via SALR potentials

“…43,44 This includes the effects of dynamic variations in the clustering of proteins which are also known to interact via SALR potentials. 26,45,46…”

A Conceptual Framework to Understand the Self-Assembly of Chemically Active Colloids

“…As a result, they self-assemble − and phase separate − into mesoscopic structures, including colloidal molecules, one-dimensional chains, two-dimensional lattices, and three-dimensional crystals. The resulting macroscopic materials are useful in protein solutions, biomineralization, colloidal swarm ink, and fabricating smart and multicomponent colloidal materials operating away from thermal equilibrium. Understanding and controlling the pairwise interaction, and thus the resulting pattern, is key to the design of smart materials with exceptional adaptivity and interactivity that can respond to different stimuli with rapid response and dynamic properties …”