Structure- and machine learning-guided engineering demonstrate that a non-canonical disulfide in an anti-PD-1 rabbit antibody does not impede antibody developability - Wei-Ching Liang; Hongkang Xi; Dawei Sun; Luigi D'Ascenzo; Jonathan Zarzar; Nicole Stephens; Ryan Cook; Yinyin Li; Zhengmao Ye; Marissa Matsumoto; Jian Payandeh; Matthieu Masureel; et al. (Taylor & Francis Group, 2024)
ABSTRACTRabbits produce robust antibody responses and have unique features in their antibody repertoire that make them an attractive alternative to rodents for in vivo discovery. However, the frequent occurrence of a non-canonical disulfide bond between complementarity-determining region (CDR) H1 (C35a) and CDRH2 (C50) is often seen as a liability for therapeutic antibody development, despite limited reports of its effect on antibody binding, function, and stability. Here, we describe the discovery and humanization of a human-mouse cross-reactive anti-programmed cell death (PD-1) monoclonal rabbit antibody, termed h1340.CC, which possesses this non-canonical disulfide bond. Initial removal of the non-canonical disulfide resulted in a loss of PD-1 affinity and cross-reactivity, which led us to explore protein engineering approaches to recover these. First, guided by the sequence of a related clone and the crystal structure of h1340.CC in complex with PD-1, we generated variant h1340.SA.LV with a potency and cross-reactivity similar to h1340.CC, but only partially recovered affinity. Side-by-side developability assessment of both h1340.CC and h1340.SA.LV indicate that they possess similar, favorable properties. Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. Our case study thus indicates that, while the non-canonical inter-CDR disulfide bond found in rabbit antibodies does not necessarily constitute an obstacle to therapeutic antibody development, combining structure- and ML-guided approaches can provide a fast and efficient way to improve antibody properties and remove potential liabilities.