Colloid models have frequently been used to successfully describe the influence of protein-protein interactions on antibody solution properties, but they suffer from inherent problems due to the anisotropic shape of the particles. The net charge required to describe electrostatic interactions is an effective quantity that cannot directly be obtained from the known molecular structure of an antibody, and the solution structure caused by excluded volume interactions is strongly overestimated at high concentrations due to the assumption of hard sphere interactions. As a result, these models have descriptive rather than predictive power. Here we present an improved, soft penetrable sphere model based on analogies to soft colloids and star polyelectrolytes that take into account the Y-shaped antibody form and the corresponding charge and ion distribution. The model not only correctly describes the concentration and ionic strength dependence of thermodynamic and collective dynamics quantities such as the osmotic compressibility and the apparent hydrodynamic radius, but also reproduces the center-of-mass static structure factor obtained in computer simulations using a weakly coarse-grained model, in which the antibody is described at an amino acid level. We demonstrate that this soft penetrable sphere model quantitatively reproduces experimental data from static and dynamic light scattering at low and high ionic strength for two well-characterized monoclonal antibodies (mAbs) using the net charges and the overall mAb dimensions directly obtained from their molecular structure.

Electrostatic interactions fundamentally govern the structure, stability, and dynamics of charged (bio)matter, yet the impact of heterogeneous and anisotropic charge distributions on the behavior of protein solutions remains elusive. Here, we introduce a versatile multiscale framework that directly connects molecular-level electrostatics to collective properties via a colloid-inspired coarse-grained modeling combined with neural network-assisted optimization. Using monoclonal antibodies as model system, our inverse design approach identifies charge patterns capable of reliably reproducing experimental structure factors, osmotic compressibility and collective diffusion coefficients in a wide region of protein concentrations. Close inspection of our data further uncovers how specific physical features and spatial arrangements of localized charge patches significantly influence the solution structure. This transferable strategy provides a predictive pathway to decode and control charge-driven interactions in complex biomolecules and, more generally, in heterogeneously-charged soft matter systems, with immediate relevance to protein formulation and biomaterials engineering.

The complexity of biomolecular interactions necessitates advanced methodologies to accurately capture their behavior in solution. In this work, we focus on monoclonal antibodies and adopt a multi-scale coarse-graining strategy for their modeling, with particular emphasis on the role of electrostatic interactions. Using scattering experiments, theoretical analysis, and large-scale computer simulations, we explicitly compare two selected case studies-markedly different in their charge distributions. Through mutually corroborating lines of evidence, we demonstrate that conventional approaches relying on electrostatic screening and implicit charge representations fail to capture the structural and thermodynamic properties of antibody solutions when strong charge heterogeneity is present, even at a moderate (amino acid) level of coarse-graining. These findings highlight the importance of a correct treatment of electrostatic interactions and ion screening for heterogeneously- and oppositely-charged colloidal and protein systems. Such considerations are essential to move beyond descriptive models towards a truly predictive framework, with direct implications for the formulation of therapeutics and the treatment of other complex soft-matter systems.

Monoclonal antibodies are among the most promising therapeutic agents in modern medicine, yet their formulation into high-concentration solutions for subcutaneous self-administration poses a major challenge. A key obstacle is the marked increase in viscosity often observed under these conditions. To gain deeper insights into this phenomenon, coarse-grained models derived from soft matter physics have been widely employed. However, these models have yet to be fully leveraged for analyzing the rheological collective properties of such systems. In this study, using molecular dynamics simulations, we directly compute the antibody solution viscosity by starting from commonly used models in which electrostatic interactions are treated through effective screened Coulomb potentials. We demonstrate that this approach fails to reproduce experimental evidence and we show, by analyzing stress correlations in the system, that it is necessary to treat the heterogeneously charged domains with explicit Coulomb interactions, also including counterions and salt ions. By thoroughly analyzing the microscopic structure of the system, we further reveal the presence of transient strongly correlated antibodies which would not be present if charges were treated implicitly, thus pointing to a prominent role of electrostatics in determining the increase in viscosity at high concentrations. By taking advantage of our realistic treatment, new approaches can be devised to ensure that antibody solutions exhibit the desired characteristics for their intended broad use and effective deployment.

Dynamic properties of γ B -crystallin are incompatible with a colloid model for attractive ellipsoids that reproduces static data and phase diagrams.

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.

Crowding effects significantly influence the phase behavior and the structural and dynamic properties of the concentrated protein mixtures present in the cytoplasm of cells or in the blood serum. This poses enormous difficulties for our theoretical understanding and our ability to predict the behavior of these systems. While the use of course grained colloid-inspired models allows us to reproduce the key physical solution properties of concentrated monodisperse solutions of individual proteins, we lack corresponding theories for complex polydisperse mixtures. Here, we test the applicability of simple mixing rules in order to predict solution properties of protein mixtures. We use binary mixtures of the well-characterized bovine eye lens proteins α and γB crystallin as model systems. Combining microrheology with static and dynamic scattering techniques and observations of the phase diagram for liquid–liquid phase separation, we show that reasonably accurate descriptions are possible for macroscopic and mesoscopic signatures, while information on the length scale of the individual protein size requires more information on cross-component interaction.

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 characterise 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 be directly related 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 (SAXS), and tracer particle-based microrheology. We analyse 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 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.

Monoclonal antibody solutions are set to become a major therapeutic tool in the years to come, capable of targeting various diseases by clever design of their antigen binding site. However, the formulation of stable solutions suitable for patient selfadministration typically presents challenges, as a result of the increase in viscosity that often occurs at high concentrations. Here, we establish a link between the microscopic molecular details and the resulting properties of an antibody solution through the characterization of clusters, which arise in the presence of self-associating antibodies. In particular, we find that experimental small-angle X-ray scattering data can be interpreted by means of analytical models previously exploited for the study of polymeric and colloidal objects, based on the presence of such clusters. The latter are determined by theoretical calculations and supported by computer simulations of a coarse-grained minimal model, in which antibodies are treated as Y-shaped colloidal molecules and attractive domains are designed as patches. Using the theoretically predicted cluster size distributions, we are able to describe the experimental structure factors over a wide range of concentration and salt conditions. We thus provide microscopic evidence for the well-established fact that the concentration-dependent increase in viscosity is originated by the presence of clusters. Our findings bring new insights on the self-assembly of monoclonal antibodies, which can be exploited for guiding the formulation of stable and effective antibody solutions.

Surface plasmon resonance is a very well-established surface sensitive technique for label-free analysis of biomolecular interactions, generating thousands of publications each year. An inconvenient effect that complicates interpretation of SPR results is the "bulk response" from molecules in solution, which generate signals without really binding to the surface. Here we present a physical model for determining the bulk response contribution and verify its accuracy. Our method does not require a reference channel or a separate surface region. We show that proper subtraction of the bulk response reveals an interaction between poly(ethylene glycol) brushes and the protein lysozyme at physiological conditions. Importantly, we also show that the bulk response correction method implemented in commercial instruments is not generally accurate. Using our method, the equilibrium affinity between polymer and protein is determined to be KD = 200 μM. One reason for the weak affinity is that the interaction is relatively short-lived (1/koff < 30 s). Furthermore, we show that the bulk response correction also reveals the dynamics of self-interactions between lysozyme molecules on surfaces. Besides providing new insights on important biomolecular interactions, our method can be widely applied to improve the accuracy of SPR data generated by instruments worldwide.

Monoclonal antibody solutions are set to become a major therapeutic tool in the years to come, capable of targeting various diseases by clever designing their antigen binding site. However, the formulation of stable solutions suitable for patient self-administration typically presents challenges, as a result of the increase in viscosity that often occurs at high concentrations. Here, we establish a link between the microscopic molecular details and the resulting properties of an antibody solution through the characterization of clusters, which arise in the presence of self-associating antibodies. In particular, we find that experimental small-angle X-ray scattering data can be interpreted by means of analytical models previously exploited for the study of poly-meric and colloidal objects, based on the presence of such clusters. The latter are determined by theoretical calculations and computer simulations of a coarse-grained minimal model, in which antibodies are treated as Y-shaped colloidal molecules and attractive domains are designed as patches. Using the predicted cluster size distributions, we are able to determine how the concentration-dependent increase in viscosity is indeed originated by the presence of small and medium-sized clusters. Our findings provide new insights on the aggregation of monoclonal antibodies, which can be exploited for guiding the formulation of stable and effective antibody solutions.

Diffusion of proteins on length scales of their size is crucial for understanding the machinery of living cells. X-ray photon correlation spectroscopy (XPCS) is currently the only way to access long-time collective diffusion on these length scales, but radiation damage so far limits the use in biological systems. We apply a new approach to use XPCS to measure cage relaxation in crowded α-crystallin solutions. This allows us to correct for radiation effects, obtain missing information on long time diffusion, and support the fundamental analogy between protein and colloid dynamical arrest.

Modeling diffusion of nonspherical particles presents an unsolved and considerable challenge, despite its importance for the understanding of crowding effects in biology, food technology and formulation science. A common approach in experiment and simulation is to map nonspherical objects on effective spheres to subsequently use the established predictions for spheres to approximate phenomena for nonspherical particles. Using numerical evaluation of the hydrodynamic mobility tensor, we show that this so-called effective sphere model fundamentally fails to represent the self-diffusion in solutions of ellipsoids as well as rod-like assemblies of spherical beads. The effective sphere model drastically overestimates the slowing down of self-diffusion down to volume fractions below 0.01. Furthermore, even the linear term relevant at lower volume fraction is inaccurate, linked to a fundamental misconception of effective sphere models. To overcome the severe problems related with the use of effective sphere models, we suggest a protocol to predict the short-time self-diffusion of rod-like systems, based on simulations with hydrodynamic interactions that become feasible even for more complex molecules as the essential observable shows a negligible system-size effect.

Parkinson´s disease is characterized by the accumulation of proteinaceous aggregates in Lewy bodies and Lewy Neurites. The main component found in such aggregates is α-synuclein. Here, we investigate how bovine eye lens crystallin proteins influence the aggregation kinetics of α-synuclein at mildly acidic pH (5.5) where the underlying aggregation mechanism of this protein is dominated by secondary nucleation of monomers on fibril surface providing an autocatalytic amyloid amplification process. Bovine α-, βH- and γB-crystallins were found to display chaperone-like activity inhibiting α-synuclein aggregation. This effect was shown to be time-dependent, with early additions of α-crystallin capable of retarding and even inhibiting aggregation during the time frame of the experiment. The inhibitory nature of crystallins was further investigated using trap and seed kinetic experiments. We propose crystallins interact with mature α-synuclein fibrils, possibly binding along the surfaces and at fibril free ends, inhibiting both elongation and monomer-dependent secondary nucleation processes in a mechanism that may be generic to some chaperones that prevent the onset of protein misfolding related pathologies.

We present a multiscale characterization of aqueous solutions of the bovine eye lens protein βH crystallin from dilute conditions up to dynamical arrest, combining dynamic light scattering, small-angle x-ray scattering, tracer-based microrheology, and neutron spin echo spectroscopy. We obtain a comprehensive explanation of the observed experimental signatures from a model of polydisperse hard spheres with additional weak attraction. In particular, the model predictions quantitatively describe the multiscale dynamical results from microscopic nanometer cage diffusion over mesoscopic micrometer gradient diffusion up to macroscopic viscosity. Based on a comparative discussion with results from other crystallin proteins, we suggest an interesting common pathway for dynamical arrest in all crystallin proteins, with potential implications for the understanding of crowding effects in the eye lens.

Concentrated solutions of monoclonal antibodies have attracted considerable attention due to their importance in pharmaceutical formulations; yet, their tendency to aggregate and the resulting high viscosity pose considerable problems. Here we tackle this problem by a soft condensed matter physics approach, which combines a variety of experimental measurements with a patchy colloid model, amenable of analytical solution. We thus report results of structural antibodies and dynamic properties obtained through scattering methods and microrheological experiments. We model the data using a colloid-inspired approach, explicitly taking into account both the anisotropic shape of the molecule and its charge distribution. Our simple patchy model is able to disentangle self-assembly and intermolecular interactions and to quantitatively describe the concentration-dependence of the osmotic compressibility, collective diffusion coefficient, and zero shear viscosity. Our results offer new insights on the key problem of antibody formulations, providing a theoretical and experimental framework for a quantitative assessment of the effects of additional excipients or chemical modifications and a prediction of the resulting viscosity.

The measurement of flow properties, such as the zero shear viscosity, of protein solutions is of paramount importance for many applications such as pharmaceutical formulations, where the syringeability of physiologically effective doses is a key property. However, the determination of these properties with classical rheological methods is often challenging due to e.g. detrimental surface effects or simply the lack of sufficient material. A possible alternative is Dynamic Light Scattering-based microrheology, where the Brownian motion of tracer particles embedded in the protein solution is monitored to access the zero shear viscosity of the sample. The prime advantages of this method compared to classical rheology are the absence of disturbing surface effects and the up to two orders of magnitude smaller protein quantities needed for an entire concentration series. This Protocol provides a detailed description of the synthesis of sterically stabilized tracer particles with surface and overall particle properties specifically designed to investigate the viscosity of protein solutions up to concentrations close to the arrest transition. These particles are tailored to avoid protein-particle as well as particle-particle aggregation at various sample conditions and thus allow for an artifact-free application of Dynamic Light Scattering-based tracer microrheology to determine the flow behaviour of biological samples. The Protocol concludes with step by step instructions for the characterization of protein solutions using a combination of the tracer particles and an advanced dynamic light scattering technique yielding the concentration-dependent zero shear viscosity.

Lysozyme is known to form equilibrium clusters at pH ≈ 7.8 and at low ionic strength as a result of a mixed potential. While this cluster formation and the related dynamic and static structure factors have been extensively investigated, its consequences on the macroscopic dynamic behavior expressed by the zero shear viscosity η0 remain controversial. Here we present results from a systematic investigation of η0 using two complementary passive microrheology techniques, dynamic light scattering based tracer microrheology, and multiple particle tracking using confocal microscopy. The combination of these techniques with a simple but effective evaporation approach allows for reaching concentrations close to and above the arrest transition in a controlled and gentle way. We find a strong increase of η0 with increasing volume fraction ϕ with an apparent divergence at ϕ ≈ 0.35, and unambiguously demonstrate that this is due to the existence of an arrest transition where a cluster glass forms. These findings demonstrate the power of tracer microrheology to investigate complex fluids, where weak temporary bonds and limited sample volumes make measurements with classical rheology challenging.

Looking at globular proteins with the eyes of a colloid scientist has a long tradition, in fact a significant part of the early colloid literature was focused on protein solutions. However, it has also been recognized that proteins are much more complex than the typical hard sphere-like synthetic model colloids. Proteins are not perfect spheres, their interaction potentials are in general not isotropic, and using theories developed for such particles are thus clearly inadequate in many cases. In this perspective article, we now take a closer look at the field. In particular, we reflect on the fact that modern colloid science has been undergoing a tremendous development, where a multitude of novel systems have been developed in the lab and in silico. During the last decade we have seen a rapidly increasing number of reports on the synthesis of anisotropic, patchy and/or responsive synthetic colloids, that start to resemble their complex biological counterparts. This experimental development is also reflected in a corresponding theoretical and simulation effort. The experimental and theoretical toolbox of colloid science has thus rapidly expanded, and there is obviously an enormous potential for an application of these new concepts to protein solutions, which has already been realized and harvested in recent years. In this perspective article we make an attempt to critically discuss the exploitation of colloid science concepts to better understand protein solutions. We not only consider classical applications such as the attempt to understand and predict solution stability and phase behaviour, but also discuss new challenges related to the dynamics, flow behaviour and liquid-solid transitions found in concentrated or crowded protein solutions. It not only aims to provide an overview on the progress in experimental and theoretical (bio)colloid science, but also discusses current shortcomings in our ability to correctly reproduce and predict the structural and dynamic properties of protein solutions based on such a colloid approach.

Short-range attractive colloids show well-defined phase behaviour in the absence of repulsions, and highly intriguing equilibrium gelation in the presence of long-range repulsions. We present the state diagram of short-range attractive colloids with repulsions that range from fully screened to intermediately ranged, i.e. longer-ranged than the attractions, but shorter ranged than the colloid size. We demonstrate that although the macroscopic phase behaviour does not change perceptibly, there is a dramatic increase of inhomogeneities once the repulsions become longer-ranged than the attractions. The interaction potentials are characterized with small angle neutron scattering, and used to renormalize the state diagram with the minimum in the interaction potential, min[U(r)], and with the reduced second virial coefficient, B-2*. We find that the extended law of corresponding states captures the onset of phase separation for shorter ranged repulsions, but fails for longer ranged repulsions. Instead, for a given model of U(r), the transition from visually homogeneous fluid to phase separation and/or gelation can be rescaled with min[U(r)] over the full range of repulsions. Finally, we suggest a generic state diagram to describe the effect of repulsions on short-range attractive systems.