Abstract
The binding of antibodies (Abs) to antigens (Ags) is a fundamental mechanism by which the immune system defends the body against harmful invaders. Understanding and predicting this interaction is critical for developing effective therapeutic Abs. In this study, we performed an extensive structural analysis of the largest available non-redundant three-dimensional (3D) database of Ab-Ag complexes. By examining the 3D structures of these complexes, we identified key amino acids (AAs) and their positions within the paratope-epitope interface. We stressed the significance of examining AAs at each complementarity-determining regions (CDR) position, as their interaction frequencies at specific sites can vary greatly from their overall tendency to appear in CDRs. Importantly, we standardized all the paratope-epitope interaction patterns identified, converting them into a format that can be easily utilized by researchers outside the structural biology field, especially those working on antibody development using machine learning. Our findings provide valuable insights for optimizing Abs, particularly in therapeutic applications, where refining the CDR loops to improve Ag binding is essential for creating effective Abs. This research may also support the development of more precise and reliable Ab-Ag prediction tools.