A series of coarse-grained models, with different levels of structural resolution, were tested to calculate the steric contributions to protein osmotic second virial coefficients (B(22,S)) for proteins ranging from small single-domain molecules to large multidomain molecules, using the recently developed Mayer sampling method. B(22,S) was compared for different levels of coarse-graining: four-beads-per-amino-acid (4bAA), one-bead-per-amino-acid (1bAA), one-sphere-per-domain (1sD), and one-sphere-per-protein (1sP). Values for the 1bAA and 4bAA models were quantitatively indistinguishable for both spherical and nonspherical proteins, and the agreement with values from all-atom models improved with increasing protein size, making the CG approach attractive for large proteins of biotechnological interest. Interestingly, in the absence of detailed structural information, the hydrodynamic radius (R(h)) along with a simple 1sP approximation provided reasonably accurate values for B(22,S) for both globular and highly asymmetric protein structures, while other 1sP approximations gave poorer agreement; this helps to justify the currently empirical practice of estimating B(22,S) from R(h) for large proteins such as antibodies. The results also indicate that either 1bAA or 4bAA CG models may be good starting points for incorporating short-range attractions. Comparison of gD-crystallin B(22) values including both sterics and short-range attractions shows that 1bAA and 4bAA models give equivalent results when properly scaled to account for differences in the number of surface beads in the two CG descriptions. This provides a basis for future work that will also incorporate long-ranged electrostatic attractions and repulsions.

The two-phase thermodynamic (2PT) model is generalized to determine the thermodynamic properties of mixtures. In this method, the vibrational density of states (DoS), obtained from the Fourier transform of the velocity autocorrelation function, and quantum statistics are combined to determine the entropy and free energy from the trajectory of a molecular dynamics simulation. In particular, the calculated DoS is decomposed into a solid-like and a gas-like component through the fluidicity parameter, allowing for treatments for the anharmonic effects in fluids. The 2PT method has been shown to provide reliable thermodynamic properties of pure substances over the whole phase diagram with only about a 20 ps MD trajectory. Here we show how the 2PT method can be used for mixtures with the same degree of accuracy and efficiency. We have examined the 2PT determined excess Gibbs free energies of Lennard-Jones (LJ) mixtures over a wide range of conditions (1 ≤ T* ≤ 3, 0.5 ≤ P* ≤ 2.5, 1 ≤ σ(BB)/σ(AA) ≤ 2, and 1 ≤ ε(BB)/ε(AA) ≤ 2), including the change of the off-diagonal LJ interactions. The 2PT determined values are in good agreement with those from Widom insertion or thermodynamic integration (TI). Our results suggest that the 2PT method can be a powerful method for understanding thermodynamic properties in more complicated multicomponent systems.

Predicting the solution viscosity of monoclonal antibody (mAb) drug products remains as one of the main challenges in antibody drug design, manufacturing, and delivery. In this work, the concentration-dependent solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0 in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30 cP) in solutions at 150 mg/mL mAb concentration. Combining molecular modeling and machine learning feature selection, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. For mAbs whose behavior was not dominated by charge effects, we observed that high viscosity is correlated with more hydrophilic and fewer hydrophobic residues in the Fv region. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low-and highviscosity mAbs.

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein–protein interactions among many antibodies. Molecular simulation is a promising method to study protein–protein interactions; however, all-atom models do not allow the simulation of multiple molecules, which is necessary to compute viscosity directly. Coarse-grained models, on the other hand can do this. In this work, a 12-bead coarse-grained model based on Swan and coworkers (J. Phys. Chem. B 2018, 122, 2867–2880) was applied to study antibody interactions. Two adjustable parameters related to the short-range interactions on the variable and constant regions were determined by fitting experimental data of 20 IgG1 monoclonal antibodies at 150 mg/mL. The root-mean-square deviation improved from 1 to 0.68, and the correlation coefficient improved from 0.63 to 0.87 compared to that of a previous model that assumed the short-range interactions were the same for all the beads. Our model is also able to calculate the viscosity over a wide range of concentrations without additional parameters. A tabulated viscosity based on our model is provided to facilitate antibody screening in early-stage design.

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