Accurate calculation of second osmotic virial coefficients of proteins using mixed Poisson–Boltzmann and extended DLVO theory

The state of proteins in aqueous solution is determined by weak, nonspecific interactions affected by pH, solvent composition, and ionic strength. Protein–protein interactions play a crucial role in determining protein stability and solubility. The second osmotic coefficient (B$_{22}$) provides insight into effective interactions between proteins in solution. Models for calculating B$_{22}$ are valuable for estimating interactions, explaining measured phenomena, and reducing experimental time. However, existing models, like the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory, assume a simple spherical shape for proteins. Owing to the fact that proteins exhibit diverse shapes and charge distributions, influencing their electrostatic properties and overall interactions, DLVO accuracy is significantly reduced for nonspherical proteins. To address this limitation, we introduce the xDLVO-CGhybr model, which combines Poisson–Boltzmann (PB) and Debye–Hückel (DH) theories to account for electrostatic interactions between proteins. PB is used for short intermolecular distances (<2 nm) with an all-atom resolution, while DH is employed for longer distances on a coarse-grained level. Additionally, xDLVO-CGhybr incorporates an improved coarse-grained Lennard-Jones (LJ) potential derived directly from the all-atom potential to capture dispersion interactions. This model improves the calculated B22 values compared to existing models and can be applied to proteins with arbitrary shape and charge under various solvent conditions (up to 1 M monovalent salt concentration). We demonstrate the application of xDLVO-CGhybr to bovine trypsin inhibitor, ribonuclease A, chymotrypsinogen, concanavalin A, bovine serum albumin, and human immunoglobulin type I proteins, validating the model against experimental data

A coarse-grained xDLVO model for colloidal protein–protein interactions

Colloidal protein–protein interactions (PPIs) of attractive and repulsive nature modulate the solubility of proteins, their aggregation, precipitation and crystallization. Such interactions are very important for many biotechnological processes, but are complex and hard to control, therefore, difficult to be understood in terms of measurements alone. In diluted protein solutions, PPIs can be estimated from the osmotic second virial coefficient, B$_{22}$, which has been calculated using different methods and levels of theory. The most popular approach is based on the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory and its extended versions, i.e. xDLVO. Despite much efforts, these models are not fully quantitative and must be fitted to experiments, which limits their predictive value. Here, we report an extended xDLVO-CG model, which extends existing models by a coarse-grained representation of proteins and the inclusion of an additional ion–protein dispersion interaction term. We demonstrate for four proteins, i.e. lysozyme (LYZ), subtilisin (Subs), bovine serum albumin (BSA) and immunoglobulin (IgG1), that semi-quantitative agreement with experimental values without the need to fit to experimental B$_{22}$ values. While most likely not the final step in the nearly hundred years of research in PPIs, xDLVO-CG is a step towards predictive PPIs calculations that are transferable to different proteins

Molecular Simulation of Quaternary Ammonium Solutions at Low Hydration Levels

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