title: Optimization of protein-protein docking for predicting Fc-protein interactions.
abstract: 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.
authors.affiliation: School of Biomedical Sciences, Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University, Perth, Australia.|Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain.|Centre for Biomedical Research, Burnet Institute, Melbourne, Australia.
authors.forename: Mark
authors.initials: M
authors.lastname: Agostino
authors.affiliation: School of Biomedical Sciences, Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University, Perth, Australia.
authors.forename: Ricardo L
authors.initials: RL
authors.lastname: Mancera
authors.affiliation: Centre for Biomedical Research, Burnet Institute, Melbourne, Australia. paul.ramsland@rmit.edu.au.|School of Science, RMIT University, Bundoora, Australia. paul.ramsland@rmit.edu.au.|Department of Surgery Austin Health, University of Melbourne, Heidelberg, Australia. paul.ramsland@rmit.edu.au.|Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Australia. paul.ramsland@rmit.edu.au.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Ramsland
authors.affiliation: Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain. juanf@bsc.es.
authors.forename: Juan
authors.initials: J
authors.lastname: Fernández-Recio
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D018360:Crystallography, X-Ray; D007141:Immunoglobulin Fc Fragments / Q000737:chemistry* / Q000378:metabolism*; D008958:Models, Molecular; D062105:Molecular Docking Simulation / Q000379:methods*; D011485:Protein Binding; D011487:Protein Conformation
chemical_list: D007141:Immunoglobulin Fc Fragments
keywords: fragment crystallizable; immune response; infectious disease; structural bioinformatics; structural immunology; structure-based design