bioRxivBioinformaticsPreprint
Better antibodies engineered with a GLIMPSE of human data
N. Lance Hepler, Andrew J. HilliD, David B. JaffeiD, Michael C. GibbonsiD, Katherine A. Pfeiffer, David L. Hilton, Melanie Freeman, Wyatt J. McDonnelliD
Abstract
GLIMPSE-1 is a protein language model trained solely on paired human antibody sequences. It captures immunological features and achieves best-in-class performance in humanization benchmarks. We demonstrate the utility of GLIMPSE-1 in humanization; engineering of antibodies for affinity, species cross-reactivity, and key developability parameters; and the creation of highly divergent functional variants with <90% sequence identity to a marketed antibody. Learning exclusively from human antibody data enables GLIMPSE-1 to enhance therapeutics and native antibodies based on patterns in the human repertoire. DisclaimerWhile we provide detailed descriptions of experimental methods and success metrics, certain methodological details of GLIMPSE-1 remain proprietary and/or redacted in this work for commercial considerations. We warmly invite researchers and potential collaborators interested in accessing GLIMPSE-1 to connect with our team via partnerships@infinimmune.com.
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