Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV

Abstract Monoclonal antibodies have emerged as key therapeutics. In particular, nanobodies, small, single-domain antibodies that are naturally expressed in camelids, are rapidly gaining momentum following the approval of the first nanobody drug in 2019. Nonetheless, the development of these biologics as therapeutics remains a challenge. Despite the availability of established in vitro directed-evolution technologies that are relatively fast and cheap to deploy, the gold standard for generating therapeutic antibodies remains discovery from animal immunization or patients. Immune-system-derived antibodies tend to have favourable properties in vivo, including long half-life, low reactivity with self-antigens and low toxicity. Here we present AbNatiV, a deep learning tool for assessing the nativeness of antibodies and nanobodies, that is, their likelihood of belonging to the distribution of immune-system-derived human antibodies or camelid nanobodies. AbNatiV is a multipurpose tool that accurately predicts the nativeness of Fv sequences from any source, including synthetic libraries and computational design. It provides an interpretable score that predicts the likelihood of immunogenicity, and a residue-level profile that can guide the engineering of antibodies and nanobodies indistinguishable from immune-system-derived ones. We further introduce an automated humanization pipeline, which we applied to two nanobodies. Laboratory experiments show that AbNatiV-humanized nanobodies retain binding and stability at par or better than their wild type, unlike nanobodies that are humanized using conventional structural and residue-frequency analysis. We make AbNatiV available as downloadable software and as a webserver.