Guiding Competitive Binding Assays Using Protein-Protein Interaction Prediction: The HER2-Affitin Use Case

Affitins are a class of small artificial proteins, designed as alternatives to antibodies for therapeutic, diagnostic, and biotechnological applications. Recent patents by Bracco Imaging S.p.A have demonstrated the potential of two engineered affitins for designing imaging probes to detect and monitor human epidermal growth-factor receptor 2 (HER2) levels in vivo. Targeting HER2 is critical, as its overexpression is linked to poor prognosis of several cancer diseases, making it a key marker for treatment strategies and diagnostic tools. Interestingly, these affitins do not compete with the commonly used monoclonal antibodies trastuzumab and pertuzumab for HER2 binding sites, allowing their concurrent use in vivo and making them suitable for imaging or diagnostic purposes. Since these two affitins compete for the same yet unidentified binding site on HER2, structural insights into these interactions are essential for facilitating the design and development of more effective diagnostic tools and treatments. In this study, we used protein-protein docking and molecular dynamics simulations to model the binding of these affitins to HER2. The stability of the predicted complexes was quantified by using the DockQ parameter, a widely used metric for evaluating protein-protein docking predictions. The docking poses were then compared with HER2 sites likely to interact with a protein partner, as predicted by the matrix of local coupling energies method. The combination of these two computational methods allowed for the identification of the most likely docking poses. Comparative analysis with HER2-protein complexes from the Protein Data Bank suggests that both affitins may bind HER2 at the same epitopes as an antibody fragment and an affibody. These findings indicate that targeted competitive binding assays could efficiently reduce the experimental efforts to map the HER2-affitin interactions. The computational approach proposed in this study not only provides insights into this specific case but also establishes a robust framework applicable for facilitating the structural modeling and interaction prediction of other affitin-protein systems.