Microfluidic Stress Device to Decouple the Synergistic Effect of Shear and Interfaces on Antibody Aggregation.

Protein denaturation and aggregation resulting from the effects of interfacial stress, often enhanced by flow and shear stress, pose significant challenges in the production of therapeutic proteins and monoclonal antibodies. The influence of flow on protein stability is closely intertwined with interfacial effects. In this study, we have developed a microfluidic device capable of exposing low volume (< 320 µL) protein solutions to highly uniform shear. To disentangle the synergistic impact of flow and interfaces on protein aggregation, we fabricated two devices composed of different materials, namely poly(methyl methacrylate) (PMMA) and stainless steel. Upon application of shear, we observed formation of protein particles in the micron-size range. Notably, The number of particles generated in the steel devices was ∼ 3.5 fold lower than in the PMMA device, hinting at an interface-mediated effect. With increasing the protein concentration from 1 to 50 mg/mL we observed a saturation in the amount of aggregates, further confirming the key role of solid-liquid interfaces in inducing particle formation. Introduction of non-ionic surfactants prevented protein aggregation, even at the highest tested protein concentration and low surfactant concentrations of 0.05 mg/mL. Overall, our findings corroborate the synergistic impact of shear and interface effects on protein aggregation. The device developed in this study offers a small-scale platform for assessing the stability of antibody formulations throughout various stages of the development and manufacturing process.

Measuring Self-Association of Antibody Lead Candidates with Dynamic Light Scattering.

In this method chapter, we provide a brief overview of the key methods available to measure self-association of monoclonal antibodies (mAbs) and explain for which experimental throughputs they are usually applied. We then focus on dynamic light scattering (DLS) and describe experimental details on how to measure the diffusion interaction parameter (kD) which is occasionally referred to as the gold standard for measuring self-association of proteins. The kD is a well-established parameter to predict solution viscosity, which is one of the most critical developability parameters of mAbs. Finally, we present a pH and excipient screen that is designed to measure self-association with DLS under conditions that are relevant for bioprocessing and formulation of mAbs. The presented light scattering methods are well suited for lead candidate selections where it is essential to select mAbs with high developability potential for progression toward first human dose.

Slow Escape from a Helical Misfolded State of the Pore-Forming Toxin Cytolysin A.

The pore-forming toxin cytolysin A (ClyA) is expressed as a large α-helical monomer that, upon interaction with membranes, undergoes a major conformational rearrangement into the protomer conformation, which then assembles into a cytolytic pore. Here, we investigate the folding kinetics of the ClyA monomer with single-molecule Förster resonance energy transfer spectroscopy in combination with microfluidic mixing, stopped-flow circular dichroism experiments, and molecular simulations. The complex folding process occurs over a broad range of time scales, from hundreds of nanoseconds to minutes. The very slow formation of the native state occurs from a rapidly formed and highly collapsed intermediate with large helical content and nonnative topology. Molecular dynamics simulations suggest pronounced non-native interactions as the origin of the slow escape from this deep trap in the free-energy surface, and a variational enhanced path-sampling approach enables a glimpse of the folding process that is supported by the experimental data.

Design of Biopharmaceutical Formulations Accelerated by Machine Learning.

In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian optimization algorithm to accelerate the design of biopharmaceutical formulations. We demonstrate the power of this approach by identifying the formulation that optimizes the thermal stability of three tandem single-chain Fv variants within 25 experiments, a number which is less than one-third of the experiments that would be required by a classical DoE method and several orders of magnitude smaller compared to detailed experimental analysis of full combinatorial space. We further show the advantage of this method over conventional approaches to efficiently transfer historical information as prior knowledge for the development of new biologics or when new buffer agents are available. Moreover, we highlight the benefit of our technique in engineering multiple biophysical properties by simultaneously optimizing both thermal and interface stabilities. This optimization minimizes the amount of surfactant in the formulation, which is important to decrease the risks associated with corresponding degradation processes. Overall, this method can provide high speed of converging to optimal conditions, the ability to transfer prior knowledge, and the identification of new nonlinear combinations of excipients. We envision that these features can lead to a considerable acceleration in formulation design and to parallelization of operations during drug development.

Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation.

Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as decreasing their development time and manufacturing costs. We highlight the emerging applications of ML in biologics discovery and development, focusing on protein engineering, early biophysical screening, and formulation. We discuss the power of ML in extracting information from complex datasets and in reducing the necessary experimental effort to simultaneously achieve multiple quality targets. We finally anticipate possible future interventions of AI in several steps of the biological landscape.

Mapping an Equilibrium Folding Intermediate of the Cytolytic Pore Toxin ClyA with Single-Molecule FRET.

The 303-residue cytolytic toxin ClyA forms a stable α-helical monomer. In the presence of detergents or membranes, however, the protein makes a large conformational transition to the protomer state, which is competent for assembly into a dodecameric cytolytic pore. In this study, we map the structure of the ClyA monomer during denaturant-induced unfolding with single-molecule Förster resonance energy transfer (FRET) spectroscopy. To this end, we probe intramolecular distances of six different segments of ClyA by placing donor and acceptor fluorophores at corresponding positions along the chain. We identify an intermediate state that contains the folded core consisting of three of the α-helices that make up the helical bundle present in the structure of both the monomer and the protomer, but with the C- and N-terminal helices unfolded, in accord with the secondary structure content estimated from circular dichroism (CD) spectroscopy. The existence of this intermediate is likely to be a consequence of the structural bistability underlying the biological function of ClyA: The terminal helices are part of the largest rearrangements during protomer formation, and the local differences in stability we detect may prime the protein for the required conformational transition.

Rapid Microfluidic Dilution for Single-Molecule Spectroscopy of Low-Affinity Biomolecular Complexes.

To enable the investigation of low-affinity biomolecular complexes with confocal single-molecule spectroscopy, we have developed a microfluidic device that allows a concentrated sample to be diluted by up to five orders of magnitude within milliseconds, at the physical limit dictated by diffusion. We demonstrate the capabilities of the device by studying the dissociation kinetics and structural properties of low-affinity protein complexes using single-molecule two-color and three-color Förster resonance energy transfer (FRET). We show that the versatility of the device makes it suitable for studying complexes with dissociation constants from low nanomolar up to 10 µm, thus covering a wide range of biomolecular interactions. The design and precise fabrication of the devices ensure simple yet reliable operation and high reproducibility of the results.

Rapid Microfluidic Double-Jump Mixing Device for Single-Molecule Spectroscopy.

We introduce a microfluidic double-jump mixing device for investigating rapid biomolecular kinetics with confocal single-molecule spectroscopy. This device enables nonequilibrium dynamics to be probed, e.g., transiently populated intermediates that are inaccessible with existing single-molecule approaches. We demonstrate the potential and reliability of the method on time scales from milliseconds to minutes by investigating the coupled folding and binding reaction of two intrinsically disordered proteins and the conformational changes occurring in a large cytolytic pore-forming toxin.

Integrated view of internal friction in unfolded proteins from single-molecule FRET, contact quenching, theory, and simulations.

Internal friction is an important contribution to protein dynamics at all stages along the folding reaction. Even in unfolded and intrinsically disordered proteins, internal friction has a large influence, as demonstrated with several experimental techniques and in simulations. However, these methods probe different facets of internal friction and have been applied to disparate molecular systems, raising questions regarding the compatibility of the results. To obtain an integrated view, we apply here the combination of two complementary experimental techniques, simulations, and theory to the same system: unfolded protein L. We use single-molecule Förster resonance energy transfer (FRET) to measure the global reconfiguration dynamics of the chain, and photoinduced electron transfer (PET), a contact-based method, to quantify the rate of loop formation between two residues. This combination enables us to probe unfolded-state dynamics on different length scales, corresponding to different parts of the intramolecular distance distribution. Both FRET and PET measurements show that internal friction dominates unfolded-state dynamics at low denaturant concentration, and the results are in remarkable agreement with recent large-scale molecular dynamics simulations using a new water model. The simulations indicate that intrachain interactions and dihedral angle rotation correlate with the presence of internal friction, and theoretical models of polymer dynamics provide a framework for interrelating the contribution of internal friction observed in the two types of experiments and in the simulations. The combined results thus provide a coherent and quantitative picture of internal friction in unfolded proteins that could not be attained from the individual techniques.

Single-molecule spectroscopy of protein conformational dynamics in live eukaryotic cells.

Single-molecule methods have become widely used for quantifying the conformational heterogeneity and structural dynamics of biomolecules in vitro. Their application in vivo, however, has remained challenging owing to shortcomings in the design and reproducible delivery of labeled molecules, the range of applicable analysis methods, and suboptimal cell culture conditions. By addressing these limitations in an integrated approach, we demonstrate the feasibility of probing protein dynamics from milliseconds down to the nanosecond regime in live eukaryotic cells with confocal single-molecule Förster resonance energy transfer (FRET) spectroscopy. We illustrate the versatility of the approach by determining the dimensions and submicrosecond chain dynamics of an intrinsically disordered protein; by detecting even subtle changes in the temperature dependence of protein stability, including in-cell cold denaturation; and by quantifying the folding dynamics of a small protein. The methodology opens possibilities for assessing the effect of the cellular environment on biomolecular conformation, dynamics and function.