Cradle: Accelerating Antibody Engineering with Cradle’s Machine Learning Models Supported by Automated HT-SPR
Presented by:
Michelle Vandeloo, MS, Research Associate and Lab Automation Engineer, Cradle
Emma Beadle, Research Associate, Cradle
Abstract: Cradle provides machine learning models that enable labs to accelerate protein engineering. We continually iterate on and improve our models using data from our automated, in-house wet lab, which generates high-quality, high-throughput data in as little as one week. The HT-SPR platform from Carterra facilitates parallel kinetic characterization of hundreds of antibody fragment variants against multiple targets, providing essential antibody engineering datasets for our models. We demonstrate this capability through the multi-round optimization of an anti-SARS-CoV-2 VHH, and the hit identification and optimization of a polyspecific snake antivenom from panning data. Furthermore, the Cradle lab’s modular approach to assay design enables us to characterize more than just binding. We present some of our additional SPR assays including studying pH-dependent binding, membrane protein targets, and epitope binning.