Rational design of oxidation-resistant antibodies through local electrostatic modulation

Oxidation is a significant degradation pathway in proteins, particularly therapeutic antibodies, that can impair function, efficacy, and stability. Understanding the sequence and structural factors that drive oxidation susceptibility is critical for incorporating chemical stability into early-stage antibody design. Here, we present a machine learning classifier trained on expert-guided structural features to assess tryptophan (Trp) oxidation risks in the complementarity-determining regions (CDR) of 187 antibodies produced internally at Genentech. The model reveals a strong correlation between local negative electrostatic potential and Trp oxidation susceptibility. A simplified two-parameter model derived from these insights achieves 79% accuracy in classifying oxidation risk, compared to 84% accuracy with the full-feature classifier. Beyond our internal dataset, the two-parameter model successfully predicted oxidation risk for all CDR Trp sites in a blind subset of eight clinical-stage antibodies. In addition, we show that modulating the electrostatic potential around the Trp side-chains through distant charge-altering mutations can significantly reduce oxidation rates. In four out of five re-engineered antibodies, oxidation rates decreased by approximately 50%, while half of these maintained binding affinity. Finally, we demonstrate that this approach can guide multi-property optimization by balancing oxidation resistance and affinity in an anti-CD33 antibody. These results establish a strong link between local electrostatic environments and Trp oxidation susceptibility, and provide a practical framework for designing oxidation resistant biotherapeutics.