The rapid engineering of high-affinity binding proteins, such as nanobodies and single-domain antibodies (sdAbs), is increasingly driven by cell-free, machine-learning-guided optimization. However, high-throughput, quantitative characterization of binding affinity remains a major bottleneck, particularly for proteins expressed in cell-free systems without purification. Here, we present High-Throughput FRET Affinity Screening Technique (HTFAST) for rapid affinity characterization of binders expressed directly in crude E. coli cell-free protein synthesis reactions. HTFAST leverages Förster resonance energy transfer (FRET) between fluorescent-protein-fused binders and dye-labeled antigens to enable real-time, quantitative measurement of equilibrium dissociation constants. We systematically optimized fluorophore pairs used and labeling parameters using the SpyTag003-SpyCatcher003 model system. Using donor-quenching and acceptor-emission FRET analyses, HTFAST reliably quantified nanomolar binding affinities in crude lysates for SpyTag003-SpyCatcher003 model system. We validated the platform for nanobodies by characterizing a CD4-binding nanobody, Nb457, and benchmarking multiple SARS-CoV-2 receptor-binding domain sdAbs, demonstrating HTFAST's ability to rank binding strengths across a range of affinities. Finally, we demonstrate that both binding partners can be expressed directly in CFPS, further streamlining screening workflows. Overall, HTFAST provides a scalable, quantitative, and cell-free-compatible approach for high-throughput affinity screening, well suited for DBTL campaigns aimed at accelerating the development of next-generation binding proteins.
Hejazi et al. (Fri,) studied this question.