Protein Science

The Protein Science team develops and applies methods for producing, purifying, and characterizing proteins to support a wide range of Biohub goals. We combine high-throughput workflows, detailed biophysical analyses, and AI-guided experimental design to accelerate discovery across diverse projects. From collaborative tool-building to functional validation of cutting-edge designs, our work sits at the interface of computation and the bench.

Expression Systems

We produce a broad range of proteins — from AI-designed antibodies to novel viral antigens — using mammalian, insect, and bacterial expression systems. Our workflows support both large-scale production for animal studies and small-scale expression for high-throughput screening and model validation. We’re also exploring cell-free expression as a rapid tool for early-stage screening.

Biochemical and biophysical characterization

We apply a range of techniques to characterize proteins and protein–protein complexes. Our toolkit includes SEC-MALS for oligomeric state, multiplexed ELISA for serological specificity, HDX-MS and SAXS for dynamics and interface mapping, and BLI for binding kinetics. We also use DSF to assess protein thermostability and are actively developing high-throughput methods for probing DNA-protein interactions.

AI-guided experimental design

We’re excited by the potential of AI to generate testable hypotheses and accelerate discovery. We’ve validated protein language models for design using high-throughput purification workflows and worked with AI agents to both constrain experimental space and evaluate designed proteins. Leveraging our high-performance computing infrastructure, we run parallelized protein modeling with tools like ESM and AlphaFold. We’re especially eager to collaborate with ML/AI experts to explore models of protein function and specificity.

Collaboration

We’re a hyper-collaborative group, with much of our success rooted in close partnerships with students, postdocs, and staff scientists. We’ve trained visiting researchers, contributed to experiments in collaborator labs, and embraced a wide range of collaborative models. If you’re interested in working together, we’d love to hear from you!