Computational docking of antibody-antigen complexes, opportunities and pitfalls illustrated by influenza hemagglutinin

Abstract

Antibodies play an increasingly important role in both basic research and the pharmaceutical industry. Since their efficiency depends, in ultimate analysis, on their atomic interactions with an antigen, studying such interactions is important to understand how they function and, in the long run, to design new molecules with desired properties. Computational docking, the process of predicting the conformation of a complex from its separated components, is emerging as a fast and affordable technique for the structural characterization of antibody-antigen complexes. In this manuscript, we first describe the different computational strategies for the modeling of antibodies and docking of their complexes, and then predict the binding of two antibodies to the stalk region of influenza hemagglutinin, an important pharmaceutical target. The purpose is two-fold: on a general note, we want to illustrate the advantages and pitfalls of computational docking with a practical example, using different approaches and comparing the results to known experimental structures. On a more specific note, we want to assess if docking can be successful in characterizing the binding to the same influenza epitope of other antibodies with unknown structure, which has practical relevance for pharmaceutical and biological research. The paper clearly shows that some of the computational docking predictions can be very accurate, but the algorithm often fails to discriminate them from inaccurate solutions. It is of paramount importance, therefore, to use rapidly obtained experimental data to validate the computational results.

Keywords: antibody modeling; antibody-antigen complexes; computational docking; hemagglutinin; influenza.

Figure 1

Schematic (a) and cartoon (b) representation of a full antibody structure. Antigens bind to the tip of the V_H and V_L domains.

Figure 2

Cartoon representation of antibodies CR6261 (left) and F10 (right). Only the CDR loops are shown at the top, not drawn to scale. The H3 loop is colored green for the X-ray structure, violet for the best PIGS model and yellow for the Rosetta models.

Figure 3

(a) Rank of the most accurate decoy (1 is the best scoring decoy, 2 the second best scoring and so forth) for each starting structure combination; (b) Rank of the most accurate decoy, presented as a percentage of all the possible starting combinations used; for example, the most accurate decoy is also the top scoring in 18% of the cases. RMSD (in Å) for the most accurate (orange and red) and best scoring (blue and cyan) decoy for CR6261 (c) and F10 (d). The best scorers are often considerably less accurate then the most accurate decoys, so choosing them as final docking solution would not be ideal. All data refer to the RosettaDock calculations.