Generative design of antibody Fc-variants with synthetic and programmable functional profiles

Abstract

Beyond antigen recognition, antibodies direct diverse immune effector functions through their constant (Fc) domain. While the Fc domain is central to antibody biology and therapeutic efficacy, our understanding of how Fc sequence encodes function remains limited, as most of Fc sequence space has not been experimentally mapped or linked to Fc-receptor engagement. Furthermore, the extensive overlap in Fc-receptor binding sites on the Fc domain has impeded efforts to engineer antibodies with tailored, multi-receptor engagement profiles that can precisely control downstream immunity. Here we introduce a novel framework for Fc engineering that integrates protein engineering with deep learning to rationally predict and engineer antibody Fc function. Using a yeast-based, aglycosylated Fc display system, we performed deep mutational scanning across the entire human IgG1 Fc domain, allowing the rational design of a diverse combinatorial library of more than 108 Fc-variants. This library was sorted based on binding to a panel of eight canonical Fc-receptors, and the resulting populations were deep sequenced to generate a high-quality dataset comprising millions of unique Fc sequences annotated with their respective Fc-receptor binding profiles. Deep learning-based classifiers trained on this dataset accurately predicted Fc-receptor binding activity from Fc sequence across all Fc-receptors tested. We further developed FcGPT, a domain-specific autoregressive protein language model pre-trained on over three million unique Fc sequences, and refined by post-training through reinforcement learning with experimental feedback (RLXF) and synthetic verifiers. FcGPT enables the computational design of novel Fc-variants with user-defined Fc-receptor binding profiles, providing a foundational tool for understanding and programming antibody-mediated immunity.