Binding affinity determination of therapeutic antibodies to membrane protein targets: Kinetic Exclusion Assay using cellular membranes for anti-CD20 antibody.

Antibody-based therapeutics targeting membrane proteins have evolved as a major modality for the treatment of cancer, inflammation and autoimmune diseases. There are numerous challenges, ranging from desired epitope expression to reliable binding/functional assays which are associated with developing antibodies for this target class. Specifically, having a robust methodology for characterizing antibody interaction with a membrane protein target is essential for providing guidance on dosing, potency and thus expected efficacy. Fluorescence-activated cell sorting (FACS) has been commonly used to characterize antibodies binding to membrane protein targets. FACS provides information about the antibody-receptor complex (antibody bound to cells) and the apparent equilibrium dissociation constant (KD') is elucidated by fitting the antibody-receptor binding isotherm as a function of total antibody concentration to a nonlinear regression model. Conversely, Kinetic Exclusion Assay (KinExA) has been used to measure solution-based equilibrium dissociation constant (KD) of antibodies. Here, KD is determined by measuring the free antibody concentration at equilibrium in a series of solutions in which the antibody is at constant concentration and the receptor (either in the membrane or the cell) is titrated. We measured the binding affinity of the anti-CD20 antibody, Rituximab, using both FACS and KinExA. There was ~25-fold difference in the binding affinity measured by these two techniques. We have explored this discrepancy through additional experiments around the mathematical framework involved in the analysis of these two different binding assays. Finally, our study concluded that KinExA enables accurate measurement of the KD for strong protein-protein interactions (sub-nanomolar values) compared to FACS.

On-chip membrane protein cell-free expression enables development of a direct binding assay: A curious case of potassium channel KcsA-Kv1.3.

Despite the significant role integral membrane proteins (IMPs) play in the drug discovery process, it remains extremely challenging to express, purify, and in vitro stabilize them for detailed biophysical analyses. Cell-free transcription-translation systems have emerged as a promising alternative for producing complex proteins, but they are still not a viable option for expressing IMPs due to improper post-translational folding of these proteins. We have studied key factors influencing in vitro folding of cell-free-expressed IMPs, particularly oligomeric proteins (i.e., ion channels). Using a chimeric ion channel, KcsA-Kv1.3 (K-K), as a model IMP, we have investigated several physiochemical determinants including artificial bilayer environments (i.e., lipid, detergent) for K-K in vitro stabilization. We observed that fusion of a 'superfolder' green fluorescent protein (sfGFP) to K-K as a protein expression reporter not only improves the protein yield, but surprisingly facilitates the K-K tetramer formation, probably by enhancing the solubility of monomeric K-K. Additionally, anionic lipids (i.e., DMPG) were found to be essential for the correct folding of cell-free-expressed monomeric K-K into tetramer, underscoring the importance of lipid-protein interaction in maintaining structural-functional integrity of ion channels. We further developed methods to integrate cell-free-expressed IMPs directly onto a biosensor chip. We employed a solid-supported lipid bilayer onto the surface plasmon resonance (SPR) chip to insert nascent K-K in a membrane. In a different approach, an anti-GFP-functionalized surface was used to capture in situ expressed K-K via its sfGFP tag. Interestingly, only the K-K-functionalized capture surface prepared by the latter strategy was able to interact with K-K's small binding partners. This generalizable approach can be further extended to other membrane proteins for developing direct binding assays involving small ligands.

Recent advances in generative biology for biotherapeutic discovery.

Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies.

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.