Modeling Antibody-Antigen Complexes by Information-Driven Docking

Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen. [Display omitted] •Accurate prediction of antibody-antigen structure is still a challenge•Hypervariable loops of antibodies can be used to bias the modeling process•Antigen epitope information, even loosely defined, is valuable for docking•HADDOCK shows better performance and generates higher-accuracy models Ambrosetti et al. demonstrate that, for the modeling of antibody-antigen complexes, using information about hypervariable loops and, when available, a loose definition of the epitope, improves the docking results. In this context, HADDOCK, which directly uses this information to guide docking, performs better than the other three software applications tested.