Optimization of protein–protein docking for predicting Fc–protein interactions

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc–protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc–protein complexes are available, numerous others have not yet been determined. Protein–protein docking could be used to investigate Fc–protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc–protein complexes. Based on the available set of X-ray structures of Fc–protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc–protein complexes, as well as in the design of peptides and proteins that target Fc.
This work was supported by grant number BIO2013‐48213‐R from Spanish Government. M.A. is a recipient of an NHMRC Early Career Fellowship (GNT1054245). We acknowledge the computational resources provided by the Australian Government through the Victorian Life Sciences Computational Initiative under the National Computational Merit Allocation Scheme (project dq3). The authors gratefully acknowledge the contribution toward this study fromthe VictorianOperational Infrastructure Support Program received by the Burnet Institute.
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Postprint (author's final draft)