Information-Driven Modelling of Antibody-Antigen Complexes

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 to improve our ability of designing efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for those complexes. We investigate here how information about complementary determining regions and binding epitopes can be used to drive the modelling process and present a comparative study of four different docking software (ClusPro, LightDock, ZDOCK and HADDOCK) providing specific options for antibody-antigen modelling. 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 both in the presence and absence of information about the epitope on the antigen.

Keywords: Antibody, docking, H3-modelling, ClusPro, ZDOCK, HADDOCK, LightDock

Suggested Citation: Suggested Citation

Ambrosetti, F. and Jiménez-García, B. and Roel-Touris, J. and Bonvin, A.M.J.J., Information-Driven Modelling of Antibody-Antigen Complexes (March 29, 2019). Available at SSRN: https://ssrn.com/abstract=3362436 or http://dx.doi.org/10.2139/ssrn.3362436

This version of the paper has not been formally peer reviewed.