Combining Scattering Experiments and Colloid Theory to Characterize Charge Effects in Concentrated Antibody Solutions.

Charges and their contribution to protein-protein interactions are essential for the key structural and dynamic properties of monoclonal antibody (mAb) solutions. In fact, they influence the apparent molecular weight, the static structure factor, the collective diffusion coefficient, or the relative viscosity, and their concentration dependence. Further, charges play an important role in the colloidal stability of mAbs. There exist standard experimental tools to characterize mAb net charges, such as the measurement of the electrophoretic mobility, the second virial coefficient, or the diffusion interaction parameter. However, the resulting values are difficult to directly relate to the actual overall net charge of the antibody and to theoretical predictions based on its known molecular structure. Here, we report the results of a systematic investigation of the solution properties of a charged IgG1 mAb as a function of concentration and ionic strength using a combination of electrophoretic measurements, static and dynamic light scattering, small-angle X-ray scattering, and tracer particle-based microrheology. We analyze and interpret the experimental results using established colloid theory and coarse-grained computer simulations. We discuss the potential and limits of colloidal models for the description of the interaction effects of charged mAbs, in particular pointing out the importance of incorporating shape and charge anisotropy when attempting to predict structural and dynamic solution properties at high concentrations.

A multi-scale numerical approach to study monoclonal antibodies in solution.

Developing efficient and robust computational models is essential to improve our understanding of protein solution behavior. This becomes particularly important to tackle the high-concentration regime. In this context, the main challenge is to put forward coarse-grained descriptions able to reduce the level of detail, while retaining key features and relevant information. In this work, we develop an efficient strategy that can be used to investigate and gain insight into monoclonal antibody solutions under different conditions. We use a multi-scale numerical approach, which connects information obtained at all-atom and amino-acid levels to bead models. The latter has the advantage of reproducing the properties of interest while being computationally much faster. Indeed, these models allow us to perform many-protein simulations with a large number of molecules. We can, thus, explore conditions not easily accessible with more detailed descriptions, perform effective comparisons with experimental data up to very high protein concentrations, and efficiently investigate protein-protein interactions and their role in phase behavior and protein self-assembly. Here, a particular emphasis is given to the effects of charges at different ionic strengths.

Chemometrics in Protein Formulation: Stability Governed by Repulsion and Protein Unfolding.

Therapeutic proteins can be challenging to develop due to their complexity and the requirement of an acceptable formulation to ensure patient safety and efficacy. To date, there is no universal formulation development strategy that can identify optimal formulation conditions for all types of proteins in a fast and reliable manner. In this work, high-throughput characterization, employing a toolbox of five techniques, was performed on 14 structurally different proteins formulated in 6 different buffer conditions and in the presence of 4 different excipients. Multivariate data analysis and chemometrics were used to analyze the data in an unbiased way. First, observed changes in stability were primarily determined by the individual protein. Second, pH and ionic strength are the two most important factors determining the physical stability of proteins, where there exists a significant statistical interaction between protein and pH/ionic strength. Additionally, we developed prediction methods by partial least-squares regression. Colloidal stability indicators are important for prediction of real-time stability, while conformational stability indicators are important for prediction of stability under accelerated stress conditions at 40 °C. In order to predict real-time storage stability, protein-protein repulsion and the initial monomer fraction are the most important properties to monitor.

Anisotropic protein-protein interactions in dilute and concentrated solutions.

Interactions between biomolecules are ubiquitous in nature and crucial to many applications including vaccine development; environmentally friendly textile detergents; and food formulation. Using small angle X-ray scattering and structure-based molecular simulations, we explore protein-protein interactions in dilute to semi-concentrated protein solutions. We address the pertinent question, whether interaction models developed at infinite dilution can be extrapolated to concentrated regimes? Our analysis is based on measured and simulated osmotic second virial coefficients and solution structure factors at varying protein concentration and for different variants of the protein Thermomyces Lanuginosus Lipase (TLL). We show that in order to span the dilute and semi-concentrated regime, any model must carefully capture the balance between spatial and orientational correlations as the protein concentration is elevated. This requires consideration of the protein surface morphology, including possible patch interactions. Experimental data for TLL is most accurately described when assuming a patchy interaction, leading to dimer formation. Our analysis supports that the dimeric proteins predominantly exist in their open conformation where the active site is exposed, thereby maximising hydrophobic attractions that promote inter-protein alignment.

Self-Interactions of Two Monoclonal Antibodies: Small-Angle X-ray Scattering, Light Scattering, and Coarse-Grained Modeling.

Using light scattering (LS), small-angle X-ray scattering (SAXS), and coarse-grained Monte Carlo (MC) simulations, we studied the self-interactions of two monoclonal antibodies (mAbs), PPI03 and PPI13. With LS measurements, we obtained the osmotic second virial coefficient, B22, and the molecular weight, Mw, of the two mAbs, while with SAXS measurements, we studied the mAbs' self-interaction behavior in the high protein concentration regime up to 125 g/L. Through SAXS-derived coarse-grained representations of the mAbs, we performed MC simulations with either a one-protein or a two-protein model to predict B22. By comparing simulation and experimental results, we validated our models and obtained insights into the mAbs' self-interaction properties, highlighting the role of both ion binding and charged patches on the mAb surfaces. Our models provide useful information about mAbs' self-interaction properties and can assist the screening of conditions driving to colloidal stability.

Electrostatics Drive Oligomerization and Aggregation of Human Interferon Alpha-2a.

Aggregation is a common phenomenon in the field of protein therapeutics and can lead to function loss or immunogenic patient responses. Two strategies are currently used to reduce aggregation: (1) finding a suitable formulation, which is labor-intensive and requires large protein quantities, or (2) engineering the protein, which requires extensive knowledge about the protein aggregation pathway. We present a biophysical characterization of the oligomerization and aggregation processes by Interferon alpha-2a (IFNα-2a), a protein drug with antiviral and immunomodulatory properties. This study combines experimental high throughput screening with detailed investigations by small-angle X-ray scattering and analytical ultracentrifugation. Metropolis Monte Carlo simulations are used to gain insight into the underlying intermolecular interactions. IFNα-2a forms soluble oligomers that are controlled by a fast pH and concentration-dependent equilibrium. Close to the isoelectric point of 6, IFNα-2a forms insoluble aggregates which can be prevented by adding salt. We show that monomer attraction is driven mainly by molecular anisotropic dipole-dipole interactions that increase with increasing pH. Repulsion is due to monopole-monopole interactions and depends on the charge of IFNα-2a. The study highlights how combining multiple methods helps to systematically dissect the molecular mechanisms driving oligomer formation and to design ultimately efficient strategies for preventing detrimental protein aggregation.

Virtual cell model for osmotic pressure calculation of charged biomolecules.

The osmotic pressure of dilute electrolyte solutions containing charged macro-ions as well as counterions can be computed directly from the particle distribution via the well-known cell model. Originally derived within the Poisson-Boltzmann mean-field approximation, the cell model considers a single macro-ion centered into a cell, together with counterions needed to neutralize the total cell charge, while it neglects the phenomena due to macro-ion correlations. While extensively applied in coarse-grained Monte Carlo (MC) simulations of continuum solvent systems, the cell model, in its original formulation, neglects the macro-ion shape anisotropy and details of the surface charge distribution. In this paper, by comparing one-body and two-body coarse-grained MC simulations, we first establish an upper limit for the assumption of neglecting correlations between macro-ions, and second, we validate the approximation of using a non-spherical macro-ion. Next, we extend the cell model to all-atom molecular dynamics simulations and show that protein concentration-dependent osmotic pressures can be obtained by confining counterions in a virtual, spherical subspace defining the protein number density. Finally, we show the possibility of using specific interaction parameters for the protein-ion and ion-ion interactions, enabling studies of protein concentration-dependent ion-specific effects using merely a single protein molecule.

Integrating All-Atom and Coarse-Grained Simulations-Toward Understanding of IDPs at Surfaces.

We present a scheme for transferring conformational degrees of freedom from all-atom (AA) simulations of an intrinsically disordered protein (IDP) to coarse-grained (CG) Monte Carlo (MC) simulations using conformational swap moves. AA simulations of a single histatin 5 peptide in water were used to obtain a structural ensemble, which is reweighted in a CGMC simulation in the presence of a negatively charged surface. For efficient sampling, the AA trajectory was condensed using two approaches: RMSD clustering (based on the root-mean-square difference in atom positions) and a "naïve" truncation, where only every 100th frame of the trajectory was included in the library. The results show that even libraries with few structures well reproduce the radius of gyration and interaction free energy as functions of the distance from the surface. We further observe that the surface slightly promotes the secondary structure of histatin 5 and more so if using explicit surface charges rather than smeared charges.

Dynamics at a Peptide-TiO2 Anatase (101) Interface.

The interface between biological matter and inorganic materials is a widely investigated research topic due to possible applications in biomedicine and nanotechnology. In this context, the molecular level adsorption mechanism that drives specific recognition between small peptide sequences and inorganic surfaces represents an important topic likely to provide much information useful for designing bioderived materials. Here, we investigate the dynamics at the interface between a Ti-binding peptide sequence (AMRKLPDAPGMHC) and a TiO2 anatase surface by using molecular dynamics (MD) simulations. In the simulations the adsorption mechanism is characterized by diffusion of the peptide from the bulk water phase toward the TiO2 surface, followed by the anchoring of the peptide to the surface. The anchoring is mediated by the interfacial water layers by means of the charged groups of the side chains of the peptide. The peptide samples anchored and dissociated states from the surface and its conformation is not affected by the surface when anchored.