Pfast™ Lead Identification And Optimization Supporting Computational Drug Discovery Technology

Computational approaches to antibody discovery can generate humanized Fab candidates faster than ever, but sequence generation is only the first hurdle. Translating AI-designed molecules into functional, well-expressed proteins demands a disciplined expression strategy, and that process is rarely straightforward. Working from three structurally similar Fab variants, a systematic screen was built around 2 operon designs, 40 vectors spanning diverse RBS and secretion leader combinations, and a single host, producing 288 unique expression strains evaluated in an automated 96-well format.

Soluble lysate fractions were analyzed by SDS-CGE under non-reducing conditions to confirm proper Fab assembly at each stage. Among Fabv1 candidates, 24 strains exceeded 350 mcg/mL soluble titer, providing a strong foundation for selecting expression strategies to apply across all three variants. Multiple Fab variants ultimately reached soluble expression above 1,000 mcg/mL, and antigen binding was confirmed by ELISA across 48 constructs. The workflow is designed to scale: the same platform can be applied to future programs without rebuilding the screening architecture from scratch.

Read the full case study to see how the expression strategy screen translated into a repeatable production workflow.