Improving antibody drug development using bionanotechnology

Characterization of membrane protein interactions by peptidisc-mediated mass photometry

Membrane proteins perform numerous critical functions in the cell, making many of them primary drug targets. However, their preference for a lipid environment makes them challenging to study using established solution-based methods. Here, we show that peptidiscs, a recently developed membrane mimetic, provide an ideal platform to study membrane proteins and their interactions with mass photometry (MP) in detergent-free conditions. The mass resolution for membrane protein complexes is similar to that achievable with soluble proteins owing to the low carrier heterogeneity. Using the ABC transporter BtuCD, we show that MP can quantify interactions between peptidisc-reconstituted membrane protein receptors and their soluble protein binding partners. Using the BAM complex, we further show that MP reveals interactions between a membrane protein receptor and a bactericidal antibody. Our results highlight the utility of peptidiscs for membrane protein characterization in detergent-free solution and provide a rapid and powerful platform for quantifying membrane protein interactions.

Simplifying complex antibody engineering using machine learning

Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with native-like intrinsic properties (e.g., high stability), greatly reducing the amount of subsequent experimentation needed to identify specific candidates that also possess desired extrinsic properties (e.g., high affinity). Additionally, supervised algorithms, which are trained on deep sequencing datasets obtained after enrichment of in vitro antibody libraries for one or more specific extrinsic properties, enable the prediction of antibody variants with desired combinations of extrinsic properties without the need for additional screening. Here we review recent advances using both machine learning approaches and how they are impacting the field of antibody engineering as well as key outstanding challenges and opportunities for these paradigm-changing methods.

Measuring Protein Aggregation and Stability Using High-Throughput Biophysical Approaches

Structure-function relationships of biological macromolecules, in particular proteins, provide crucial insights for fundamental biochemistry, medical research and early drug discovery. However, production of recombinant proteins, either for structure determination, functional studies, or to be used as biopharmaceutical products, is often hampered by their instability and propensity to aggregate in solution in vitro. Protein samples of poor quality are often associated with reduced reproducibility as well as high research and production expenses. Several biophysical methods are available for measuring protein aggregation and stability. Yet, discovering and developing means to improve protein behaviour and structure-function integrity remains a demanding task. Here, we discuss workflows that are made possible by adapting established biophysical methods to high-throughput screening approaches. Rapid identification and optimisation of conditions that promote protein stability and reduce aggregation will support researchers and industry to maximise sample quality, stability and reproducibility, thereby reducing research and development time and costs.