ADAPT-M: a workflow for rapid, quantitative in vitro measurements of enriched protein libraries
ADAPT-M is a workflow combining design and high-throughput experimentation. It overcomes the testing bottleneck and enables rapid quantitative affinity measurements of thousands of designer proteins enriched from yeast s
Protein-protein interactions underpin most cellular processes, and engineered binders present powerful tools for probing biology and developing novel therapeutics. However, scalable, quantitative characterization of large numbers of candidates remains a major bottleneck. The ADAPT-M workflow addresses this challenge by integrating design and high-throughput experimentation. By leveraging microfluidics and parallelized experimentation, ADAPT-M enables rapid, quantitative affinity measurements of thousands of designer proteins directly from enriched display libraries in under one week, without requiring gene synthesis or hands-on protein purification. This approach has been successfully applied to a computationally designed library targeting the SARS-CoV-2 Omicron BA.1 receptor binding domain. The results show that ADAPT-M recovered most highly enriched variants and revealed that many display-enriched binders lacked measurable binding in vitro, highlighting the limitations of screening alone. The workflow enabled the quantitative characterization of dozens of binders in parallel and the selection of lead candidates for structural analysis. Notably, structural and mutational studies revealed that designed binding interfaces were preserved despite engaging alternative epitopes. The ADAPT-M workflow bridges the gap between screening and scalable in vitro validation, accelerating protein binder discovery and supporting data-driven protein engineering. By providing a rapid and efficient means of characterizing large numbers of protein binders, ADAPT-M has the potential to significantly impact the development of novel therapeutics. However, it is essential to note that ADAPT-M is a research-use-only workflow and should not be used for clinical applications without further validation and regulatory approval.