Improving Antibody-Antigen Interaction Prediction Through Flexibility with ESMFold

Sara Joubbi, Giuseppe Maccari, Giorgio Ciano, Alessio Micheli, Paolo Milazzo, Duccio Medini

Abstract: Antibodies are essential proteins in the immune system due to their capacity to bind to specific antigens. They also play a critical role in developing vaccines and treatments for infectious diseases. Their complex structure, with variable regions for antigen binding and flexible hinge regions, presents challenges for accurate computational modeling. Recent advancements in deep learning have revolutionized protein structure prediction. Despite these advancements, predicting interactions between antibodies and antigens remains challenging, mainly due to the flexibility of antibodies and the dynamic nature of binding events. This study uses fingerprint-based methodologies that incorporate ESMFold confidence scores as a flexibility feature to model Ab-Ag interactions. Our methodology shows how including flexibility has improved Ab-Ag interactions by 3%, achieving an AUC-ROC of 91%.