Exploring diverse routes to high-affinity-antibody variable domains through deep-sequencing-informed machine learning

Abstract

The integration of in vitro selection, deep sequencing, and machine learning (ML) has recently been developed as a powerful strategy for discovering functional antibodies. However, how training data composition and ML search space design influence the identification of high-affinity variants remains unclear. Here, we aimed to optimize ML-integrated directed evolution for functional antibody discovery by selecting training data from deep sequencing analysis. By performing phage display selection using camelid heavy-chain antibodies (VHHs), we demonstrated that early-round data, retaining more binding-negative variants, can be superior for training models to identify high-performance VHHs. We also investigated a lead-independent ML search space design by focusing on conserved residues in final rounds, successfully identifying variants with higher affinities than those from lead-based maturation (KD = 7.9 nM). These findings demonstrate that training data selection and search space design are critical for successful ML-guided antibody engineering and provide diverse pathways for discovering high-affinity VHH variants.